Quant Conference 2016 - Macquarie · 2016-07-05 · Macquarie Wealth Management Quant Conference 2016 5 July 2016 2 Introducing the Macquarie Quant Team Global Presence The Macquarie

Please refer to page 23 for important disclosures and analyst certification, or on our website

www.macquarie.com/research/disclosures.

GLOBAL

5 July 2016

Quant Conference 2016 Blending the old and the new On the 27

th and 28

th of June we held our 7

th Global Quant conference in Hong

Kong. This two day event attracted 150+ investment professionals to explore

the latest ideas in investing. In this report we have compiled a summary of the

conference proceedings and discussions which took place.

We would like to thank all speakers and attendees for their participation in the

discussions and making this year’s conference a success. We also thank

Thomson Reuters for sponsorship of parts of the event.

A look into the future

This year we hosted a technology session that included presentations from

Terra Bella / Google: Focussing on Satellite imaging and the game-changing

ability to get real-time, more accurate economic data such as global oil

supply.

Aidyia: The advancement of Artificial Intelligence and how it can be used to

build a hedge fund.

Quantopian: Creating a crowd-sourced hedge fund and the challenges of

allocating capital and blending multiple strategies together.

But let’s not forget the fundamentals

Leading academics focussed on core investment strategies and signals.

Richard Sloan: On the demise of systematic value investing.

Joseph Gerakos: How Cash Profitability is a superior measure of company

quality compared to Accruals and Operating Profitability.

Other Key Themes

Multi-horizon modelling: Building a model incorporating factors with different

horizons, that are asymmetric, have different sector and time varied payoffs.

Quant investing in China – How to best build a quant process for the

Chinese equity market.

Sustainability and the Investment Process – A practitioner’s example of

how to include sustainability in the investment process.

Shareholder activism and Social norms – Investment stewardship is

becoming increasingly important for asset managers. Relatedly another

presentation discussed the benefits of creating an environment where

management feel trusted by the board and shareholders.

Factor Premia and Wealth creation – How factor premia can significantly

improve the long-term road to wealth creation.

Systematic Defaults and Risk Management – Presentations highlighting the

benefits of risk management and how default risk can forecast future returns.

Investor herding– Impact of herding on stock performance.

Trading for Status – Time-varying demand and trading in local Chinese

stocks can be explained by investors concerns over status.

http://www.macquarie.com/research/disclosures

Macquarie Wealth Management Quant Conference 2016

5 July 2016 2

Introducing the Macquarie Quant Team Global Presence

The Macquarie Quant team comprises 15 professional staff, based in six countries around the

world. With offices located in Sydney, New York, London, Hong Kong and Johannesburg, we

are well placed to provide quantitative advice and products in all major markets around the

world.

Fig 1 The Macquarie Quant Team

Source: Macquarie Research, July 2016

An award-winning team

Consistently top rated in Greenwich Institutional Investors Survey.

Ranked #1 quantitative research team for Asian research for last 8 years in a row by

Greenwich Associates Institutional Investors Survey.

Ranked #1 for Australian research for almost 2 decades.

Our products

Our team is divided into four areas of specific expertise. All the teams work closely together to

leverage off the skills and experience of each group. Alpha generation and client service are

central to each team’s offering.

Fig 2 What we do


Risk and Portfolio Products

Implementation, structuring and hedging of quant and thematic strategies

Portfolio analysis and backtesting: realistic portfolios that are risk and liquidity aware

Capacity analysis: stress testing portfolios

Bespoke risk and portfolio analysis

Global Quantitative Research

Alpha Idea generation

Market, strategy and event analysis

Alpha Model construction

Publications – Quantamentals, Global dynamics, Dynamics, Academic Abstracts, Risky Business, Fat Tales

Bespoke Alpha idea analysis

Custom Products

Fully customisable quant screens

Rapid response for customised data and analysis

Regular feeds, comp tables, tearsheets

Custom client models, reports and charts

Bespoke client project work

Technology Products

Design and development of customised technology solutions

Quantitative technology and systems consulting

Database construction

Data cleaning for global data

The Macquarie

Global quant team

provide quantitative

advice and products

in all major markets

around the world

At the core of our

product offering are

quantitative research

and alpha generation

ideas, which are

extensively

incorporated into our

other quant products


5 July 2016 3

Conference program Day 1 - Monday 27 June

08:50-09:00

Conference Welcome Address

Peter Redhead, Global Head of Research, Macquarie

Session 1: Asset Pricing

09:00-09:45

Accruals, Cash Flows and Operating Profitability in the Cross Section of Stock Returns

Joseph Gerakos, Associate Professor of Accounting and the David Booth Faculty Fellow, University of Chicago Booth School of Business

09:45-10:30

Integrating ESG into Valuation Models and Investment Decisions

Willem Schramade, Analyst Sustainability & Valuation, Robeco Asset Management

Session 2: Special Session on Big Data

10:50-11:50

Big Data & Small Satellites - Economic Insights from Space: How a Google Moonshot is Attempting to Change the Nature of Satellite Imaging and Revolutionize Economic Transparency

Patrick Dunagan, Strategic Partnerships Manager, Terra Bella / Google

Session 3: Lunch Panel - Fintech (Data and Technology)

12:10-12:50

How and Why Artificial Intelligence Will Come to Dominate Finance

Ben Goertzel, Chief Scientist, Aidyia Limited

12:50-13:30

The Statistical Challenges of Constructing an Crowd-Sourced Algorithmic Portfolio

Delaney Granizo-Mackenzie, Academic Lead and Engineer, Quantopian

Session 4: Corporate Governance

13:50-14:35

Social Norms and Finance

Gilles Hilary, The Mubadala Chaired Professor in Corporate Governance and Strategy, INSEAD (Singapore)

14:35-15:20

Shareholder Activism and Engagement in Asia

Pru Bennett, Head of Investment Stewardship APAC, Blackrock

Session 5: Asset Pricing

15:30-16:15

Systemic Default and Return Predictability in the Stock and Bond Markets

Shaojun Zhang, Assistant Professor of Finance, The University of Hong Kong, The Ohio State University

16:15 Day 1 Closing Address


5 July 2016 4

Day 2 – Tuesday 28 June 08:50-09:00 Introduction

Jake Lynch, Head of Research, Asia, Macquarie

Session 6: Accounting / Fundamental / Behavioural Investing

09:00-09:45 Information Environment, Systematic Volatility and Stock Return Synchronicity

Steven Wei, Associate Professor of Finance, The Hong Kong Polytechnic University

09:45-10:30 A Critique of Formulaic Value Investing

Richard Sloan, Emile R. Niemela Chair in Accounting and International Business, Haas School of Business, University of California Berkeley

Session 7: Risk Management / Asset Allocation / Investing in China

10:50-11:35 Dynamic Risk Management and Asset Allocation

Harold Kim, Founder and Chief Executive Officer, Neo Risk Investment Advisors

11:35-12:20 Quant Equity Trading in China

Hua He, Professor of Financial Practice, CKGSB; Founder and Chief Executive Officer, Shanghai Nine Martingale Investment Management, LP Grace Xing Hu, Assistant Professor, School of Economics and Finance, The University of Hong Kong

Session 8: Lunch Panel - Client Session on Portfolio Construction

12:40-13:10 Multi-Horizon Modelling: Balancing Flexibility, Stability and Decay

Mark Nebelung, Co-Head of Global Systematic Solutions, Principal Global Equities

13:10-13:40 Stalking the Herd: Can Investors Profit from Changes in Mutual Fund Holdings

Giuliano De Rossi, Head of European Quantitative Research, Macquarie

13:40-14:25 A Wealth Management Perspective on Factor Premia and the Value of Downside Protection

Stefano Cavaglia, Head of Investment Research, Findex

Session 9: Accounting / Fundamental / Behavioural Investing

14:45-15:30 Option Return Predictability

Jie Cao, Associate Professor of Finance, The Chinese University of Hong Kong Xintong Zhan, Assistant Professor of Finance, Erasmus University Rotterdam; Academic Advisor, Rayliant Global Advisors

15:30-16:15 Trading for Status

Wenxi Jiang, Assistant Professor of Finance, The Chinese University of Hong Kong

The following is our summary of the views expressed by our external speakers. It should be noted that the views expressed by the speakers may not necessarily be the views of Macquarie.


5 July 2016 5

Accruals, Cash Flows, and Profitability in the Cross Section of Returns Joseph Gerakos

Associate Professor of Accounting and the David Booth Faculty Fellow, University of Chicago Booth School of Business

This work explores the cash operating profitability factor and its ability to forecast stock

returns. The analysis is performed on the US market from July 1963 to December 2014.

Joseph started his presentation by discussing earnings and expected returns and how in the

60s, Ball and Brown showed that net income predicted the cross-section of average returns.

However, Fama and French in the 90s showed that in the presence of size and book to

market, net income added little incremental information. In more recent times, Novy-Marx

(2013) found that firms with higher gross profitability earned higher returns. This measure

performs better than other earnings indicators such as net income, EBITDA, accruals and

cash flow etc. due to it being a “cleaner” measure of economic profit than bottom line net

income.

Next, Joseph showed several slides formally defining the gross profitability measure and the

operating profitability measure. The operating profitability measure (by taking out “selling,

general & administrative expenses” item) is an improved measure to predict average cross

sectional returns compared to the gross profitability measure. He then moved on to discuss

accruals in detail by linking accruals, profitability and average returns together i.e. average

returns increase profitability, however they decrease accruals. Since Sloan’s seminal work in

1996, the accruals anomaly is not explained by Fama & French’s three-factor model, Fama &

French’s five-factor model that includes a profitability factor, the Novy-Marx gross profitability

factor or the Hou, Xue and Zhang (2015) q-factor model. In fact, the accrual anomaly

strengths increase when controlling for operating profitability. Furthermore this anomaly is

more apparent among smaller stocks.

For the rest of the presentation, Joseph showed:

(1) Cash-based operating profitability, a measure of profitability that is devoid of

accounting accruals adjustments, is better at explaining the cross section returns

than gross profitability, operating profitability and net income

(2) Cash-based operating profitability subsumes the accrual anomaly. Accruals predict

returns because of an omitted variable problem in that once you control for the

variable that truly predicts return, accruals do not matter.

(3) Cash-based operating profitability explains expected return as far as 9 years ahead.

The persistence of the signal is investigated from performing lagged Fama & French

regression up to 10 years.

Fig 3 The value of using Cash based operating profitability. This measure achieves the highest Sharpe ratio.

Source: Joseph Gerakos, University of Chicago Booth School of Business, July, 2016

To conclude the presentation, Joseph showed that investors in fact would be better off by

adding the cash-based profitability factor (as shown in the above figure) rather than trading on

both accruals and profitability strategies together.


5 July 2016 6

Integrating ESG into Valuation Models and the Investment Decisions: the Value-Driver Adjustment (VDA) Approach Willem Schramade

Analyst Sustainability & Valuation, Robeco Asset Management

In Willem’s presentation he gave a thorough overview on how Robeco Asset Management

made ESG integration work. They do not add ESG at the end, but rather, it is very much an

integral part of their analysis. He talked about a “Value-Driver Adjustment” approach which

ties in with traditional valuation approaches by linking ESG issues to value drivers via the

impact on business as well as their competitive position.

Willem started the presentation by discussing the definition of sustainability and why it

matters to investors. The increase in societal pressure causing client demand as well as

externalities increasingly being internalised impacting business models and in turn affecting

shareholder value means we need to know more. Willem dispelled some common myths that

ESG integration is really not like other types of SRI because it aims to outperform. It is also

not a box ticking exercise as ESG integration “hits the models”. Finally, material ESG issues

matter for stock performance and it is not simply ratings and screens.

Willem spent a good amount of time discussing each of the three steps (see figure below)

they took to integrate ESG into all stages of the investment process.

Fig 4 Steps to integrate ESG

Source: Willem Schramade, Robeco Asset Management, July, 2016

For Robeco Asset Management, the results are encouraging so far. Average price target

impact of ESG factors is 5% overall on their equities funds and the target price changes

ranged from -23% to 71%. Through this integration, Willem said their investment team has

gained a more in-depth understanding of companies on what is material per sector, as well as

a much clearer view on risk to make better informed decisions. The key message here is that

a competitive edge should result in stronger Value-Drivers.

Figure below shows the top 5 material issues identified. Willem said ESG for them has been a

major consideration in 28% of cases and decisive in 9% of cases. Approximately 1/3 resulted

in SELL and 2/3 in BUY decisions.

Fig 5 Top 5 material issues

Source: Willem Schramade, Robeco Asset Management, July, 2016

Finally, Willem spoke about the way forward for ESG integration. More competition between

ESG data providers and better use of big data will make underlying value drivers more visible.

This will drive better integration of sustainability into corporate strategy and investment

decisions. At the corporate level, the result is for a deeper and more comparable reporting

with higher assurance. This will benefit everyone.

Step 1: Identify and

focus on most material issues

Step 2: Analyze impact

of material factors

Step 3: Quantify to adjust value

driver assumptions

Better informed decisions

IssueUsed in percentage of

cases

Innovation Management 39%

Corporate Governance 27%

Environmental Management 20%

Supply Chain Management 17%

Energy Efficiency 16%


5 July 2016 7

Big Data & Small Satellites – Economic Insights from Space: How a Google Moonshot is Attempting to Change the Nature of Satellite Imaging and Revolutionise Economic Transparency Patrick Dunagan

Strategic Partnerships Manager, Terra Bella, Google

The Main theme of this presentation is demonstrating how technology is changing the

landscape of finance where future applications are limited only by the imagination. Terra Bella

is a Google company pioneering in the search of patterns of change in the world to address

economic and environmental challenges through the use of satellite imaging and combining

cutting edge technology in computer vision, pattern recognition and machine learning.

In order to make sense in our physical world there are roughly three main steps

(1) Collection of data

(2) Finding patterns in the data

(3) Making sense of the data

The first step involves using satellite technology to collect high resolution image data. Terra

Bella currently has 3 satellites in orbit and this allows them to capture sites once a week. The

aim is to put more than 10 satellites within the next year to achieve daily site visits. Their

vision is to have “dozens” of satellites in space for multiple collections of a site per day. The

advantage of using their own satellite is that they can control all aspects of the data collection

which is important to make the analytics step successful. The second step is to use computer

vision and pattern recognition techniques to make sense of the data collected. Once these

patterns are identified and features are extracted the final step involves interpreting and

making sense of them.

In the presentation, Patrick showed examples of (1) Vehicle detection of a car import lot in the

US, (2) coal mass stockpile in Australia and (3) crude oil inventory in the oil fields. He used

Iran as a case study and showed that by combining Terra Bella onshore terminal estimates

with AIS data, one can obtain a more complete picture of global crude oil storage levels.

In the second half of the presentation Patrick demonstrated a web based beta platform to drill

into the oil data in greater detail. The impressive GUI allows users to easily:

Explore areas of interest visually

Create charts with dashboards to easily research how various data changes through time

Download all data into csv files for those that want deeper understanding of data

Ability to customise existing areas

Terra Bella’s technology is still at an early stage of development but it is impressive to see

how far it has come. Some of the future targets could include major infrastructures, ports,

airports, mines etc and potential applications include:

Helping economists and traders measure leading global economic indicators by identifying

supply chain bottlenecks and operational inefficiencies to monitor the flow of goods

Aiding in emergency and disaster relief

Tracking development of infrastructure to identify areas of opportunity and risks


5 July 2016 8

How and Why Artificial Intelligence Will Come to Dominate Finance Ben Goertzel

Chief Scientist, Aidyia Limited

Ben gave a fascinating presentation on Artificial General Intelligence (AGI), helped by his

humanoid robot, Han. Han can mimic human expressions, making him very engaging. Han

gave the introduction to Ben’s presentation, demonstrating a range of facial expressions and

introducing some of the ideas explored later by Ben.

Ben gave an overview of the difference between (narrow) Artificial Intelligence (AI) and AGI.

Humans have general intelligence – given a new context they can easily adapt to the

situation, using prior knowledge in a new way. Limited AI requires modification (‘brain

surgery’) to adapt to a new environment or ‘context’. It is thus more suitable for specific tasks

or solving specific problems. For example, an AI capable of truck driving would be different to

an AI driving a motorbike. However a human can more easily adapt to the new context

without the need for the same extensive re-learning.

Fig 6 A demonstration of the capabilities of Han the robot

Source: Aidyia, July, 2016

AI is currently being used in finance; however a limitation of the current AI technology is the

need for retraining to incorporate changes in the environment. This reduces its applicability to

longer range forecasting. The further a forecast is in the future, the more of the context has

to be understood. The use of AGI will enable the environment context to be understood by

the AI system and therefore be able to adapt to new environments.

Ben outlined how the AI system at Aidyia Limited uses the OpenCog AI software; software

that was originally developed for broad-based research in genomic and robotics. This is in

contrast to the rather narrow AI which has been used in finance for a long time. This helps

enable it to identify market inefficiencies different to other AI software already in use. In

particular, it is aims to predict stock prices weeks in advance, rather than the shorter term by

most other AI systems.

Ben predicts that in the near future, most money will be managed by AI systems, removing

the human emotion from the environment.


5 July 2016 9

The Statistical Challenges of Constructing an Crowd-Sourced Algorithmic Portfolio Delaney Granizo-Mackenzie

Academic Lead and Engineer, Quantopian

How does one best choose a trading strategy that will be profitable in live trading, given only

its backtested returns?

Delaney and the researchers at Quantopian found attributes of trading strategies

which are most likely to lead to strong live performance.

Quantopian provides an environment which allows anyone to create and test trading

strategies. This environment consists of historical data sets along with a backtesting

framework and functionality for saving and deploying trading strategies. Users retain the

Intellectual Property behind their strategies, with Quantopian investing capital in the best.

In order to allocate capital it is necessary to determine which strategies have the best

potential.

With over 80,000 users and 3.5 million simulations, Quantopian has a large number of

potential strategies to evaluate and a perfect dataset to run empirical analysis on. Delaney

discussed a paper written by Quantopian where the in-sample (IS) and out-of-sample (OOS)

performance was compared using a number of metrics. It was found that on most standard

performance metrics, the IS performance was not predictive of the OOS performance.

Fig 7 Comparison of In Sample vs Out Of Sample performance using various performance metrics

Source: Delaney Granizo-Mackenzie,Quantopian, July, 2016

Instead a machine learning algorithm was used to select the features of the strategies that

were most predictive.

It was found that many measures of risk were good predictors of OOS returns. Attributes

such as Skewness, Kurtosis, Tail ratio etc were better predictors of OOS performance than

ratios such as Sharpe ratio and Information ratio.


5 July 2016 10

Social Norms and Finance Gilles Hilary

Professor of Accounting and Control at INSEAD

Professor Hilary talked about trust in management and showed by placing greater trust in

management of a company, by removing onerous and costly contracts as well as doing less

in the form of monitoring, management reciprocates with better performance. He argued that

trust is good for you!

He mentioned some earlier papers in which he found that religion influences corporate

decision making, “Does Religion Matter in Corporate Decision Making in America?” – (Hilary

and Hui. 2009). He finds that firms located in counties with higher religiosity have less risk

exposure as measured by volatility.

Furthermore in a later paper “Trust and Contracting” (Hilary and Huang 2015) he finds that

companies located in countries where trust is more prevalent have less agency problems.

He defines trust as the subjective probability that an individual assigns to the events of a

potential counterparty performing an action that is beneficial to that individual. He argues that

US firms located in regions of ‘greater trust’, are subject to weaker direct monitoring but are

equally less likely to engage in empire building. This results in their companies having higher

valuations and profit margins.

He referred to research that suggests that trust is inherited, but also influenced by social

factors such as administrative institutions, ethnic diversity as well as religious background.

Furthermore trust, especially in children is correlated to the prevailing attitudes in the region.

He concludes that firms in US counties where trust is higher:

Are less contract intensive

Have less direct monitoring

Are less likely to engage in empire building

Have higher profitability

And higher valuation

The result is robust to different specifications and control variables as detailed in the original

paper, Regulation through Social Norms, Gilles Hilary and Sterling Huang.

The question was raised around the fact in some cases the board or CEO may not from the

locality. In other words they may have come from a low trust environment and are not in a

high trust environment.

Professor Hilary noted that we generally relocate to areas in which there are similar values

and most importantly there is research to show that we adapt to the prevailing environment.

In other words if we come from a low trust environment and move to high trust environment,

our preferences will change.


5 July 2016 11

Shareholder Activism and Engagement in Asia Pru Bennett

Head of Investment Stewardship APAC, Blackrock

Pru Bennett talked about Investment Stewardship principles and how Blackrock has

embraced them and how they implement them globally.

The stewardship principles were born from the Financial Crisis around concerns that

institutional investors where too passive and should have taken a more assertive role in

steering their investments (i.e. firms) long before such a crisis arose. In February 2009 the

Walker Review commenced and was largely focused on banks. The initial UK Steward Code

was published in July 2010 and since December 2010 all UK authorised asset managers are

required to produce a statement of commitment to the Stewardship Code or explain why it is

not appropriate to their business model.

Japan, Malaysia, Hong Kong and Taiwan all have Stewardship Codes. Korea, Thailand and

Singapore are considering such codes while Australia is notably without a stewardship code.

The codes are strongly aligned with the UK code. The key seven principles of the code are as

follows; Investors Should,

1. Establish and report to their stakeholders their policies for discharging their

ownership responsibilities.

2. Monitor and engage with their investee companies.

3. Establish clear policies on when to escalate their engagement activities.

4. Have clear policies on voting.

5. Be willing to act collectively with other investors when appropriate.

6. Report to their stakeholders on how they have discharged their duties.

7. When investing on behalf of clients, have polices on managing conflicts of interest.

The key advantage of such an approach is that it creates an obligation for managers to

initiate dialogue with companies in which they have an interest in. This results in them

becoming accountable to investors.

Furthermore this engagement encourages and coerces companies to facilitate shareholder

interaction and engagement. That said it was highlighted that there are those that feel the

code leads to ineffective and time wasting interactions with the company. However that is not

the view of Blackrock and they believe their engagements have been productive and helped

protect their investments.

Blackrock engage with companies for four main reasons:

1. During the voting process when clarification of company information is required

2. There has been an event at the company that has impacted or may impact long-term

company value

3. The company is in a sector or market where there is a thematic governance issue

material to shareholder value

4. We have identified the company as lagging its peers on environmental, social or

governance matters that could impact economic value

The decision to take a position on a particular issue can be initiated through a review of a

proxy advisory firms analysis. If the issue is straightforward Blackrock have clear guidelines

on how to act. If the issue is more complex, it is flagged for further review and analysis which

may require input from the portfolio managers or it may be necessary to engage with the

company board. Votes are then executed through an electronic platform and in some cases

through a proxy in cases where there is ‘exceptional conflict’.


5 July 2016 12

Systemic Default and Return Predictability in the Stock and Bond Markets Shaojun Zhang

Assistant Professor of Finance, The University of Hong Kong & The Ohio State University

Shaojun’s talk looked at two dimensions, the first was a construct to estimate the joint

probability that x% of firms in the S&P 500 will default in the subsequent year. The second

part was to explore this measure and what is means for subsequent performance. She finds

that this measure while being an indicator of higher levels of market distress and the potential

of default, also predicts subsequent performance of both equities and bonds.

The idea is to measure the probability that at least 1%, 2% or 5% of firms will default in the

next year. The measure is estimated using a generalization of the CAPM-style Merton model

of Coval, Jurek and Stafford (2009) and includes both a market term and an idiosyncratic

term.

This probabilistic measure does not mechanically embed a default risk premium yet it is

interesting to note that it appears to capture this premium to some extent in that the measure

is able to predict subsequent equity and bond performance. Most importantly it accounts for

correlated defaults, allows firm-level heterogeneity and relies on equity and accounting data

for estimation. It therefore has a long time series from 1973.

Shaojun finds that a one standard deviation increase in the measure is associated with a 30

bps increase in the default spread and a 0.2% increase in the realised default rate.

Furthermore as can been seen in the chart below, this measure largely coincides with

recessionary periods in the United Sates indicated by the grey bars.

Fig 8 Default Measure and the economic cycle

Source: Shaojun Zhang (Hong Kong University & Ohio State University), July 2016

When assessing its ability to predict subsequent performance she found that in sample tests it worked well in large cap and small cap and even in the treasury market. However in out of sample tests this approach worked only in BAA bonds and small cap equities.


5 July 2016 13

Information Environment, Systematic Volatility and Stock Return Synchronicity Steven Wei

Associate Professor, The Hong Kong Polytechnic University

Stock price synchronicity, defined as the R2 from asset pricing regressions, is used to

measure the amount of firm-specific information impounded in stock prices. Prior research

has focused mainly on the importance of idiosyncratic volatility as the driver of R2 [R2 =

Systematic / (Systematic + Stock Specific)], however the authors argue in their paper that

systematic volatility plays an important role and should not be discounted.

R2 can be decomposed into systematic and stock-specific volatility, where higher R2 is driven

by either higher systematic volatility or lower stock specific volatility. Professor Wei first builds

a theoretical framework and then empirically test their hypothesis for Chinese stocks by

comparing the information environment around reporting season and non-reporting periods.

Key results of their empirical analysis are:

1) At firm level, both R2 and systematic volatility become smaller in a better information

environment that in a poorer one. The reduced R2 dominantly comes from the

reduced systematic volatility;

2) Stock market volatility is lower in a better information environment than in a poorer

one

Fig 9 Univariate Analysis for the dynamic pattern of firm-level R2

Note: *** denotes statistical significance Source: Jing Wang, Steven Wei and Wayne Yu, July 2016

Professor Wei examined return synchronicity in different market periods including inside and

outside earnings season. He finds that return synchronicity is lower in the earnings season

when the information disclosure intensity is dramatically increased. The dominant effect for

this dynamic pattern is coming from the systematic volatility rather than the idiosyncratic

volatility, and this pattern is more pronounced for older firms whose firm-specific uncertainty is

relatively low.

In conclusion Professor Wei finds that stock return synchronicity decreases (increases) when

the general information environment becomes better (worse). He also finds that systematic

volatility would fluctuate with the general information environment when investors learn

across assets.


5 July 2016 14

A Critique of Formulaeic Value Investing Richard Sloan

Emile R Niemela Chair in Accounting and International Business, Haas School of Business, University of California Berkeley

In his presentation, Prof. Sloan started by discussing the failure of traditional value metrics

like Price to Book which is now well documented within the literature. Interestingly they show

that underperformance of growth stocks and not outperformance of value stock has been the

driver of value premium and especially within the small-caps.

Next Prof Sloan discussed the construction methodologies of some of the largest Value ETF

providers who use measures like Price to Book, Price to Earnings and Price to Sales and

Dividend Yield to construct value portfolios. Their argument is that it is incredibly difficult to be

successful simply by mimicking other industry participants, i.e. alpha is a zero sum game.

Going back to Graham & Dodd principles who argue that undervalued stocks should benefit

from increase in prices once investors realise that these companies are trading below their

intrinsic value, they ask the following two questions:

1) Do valuation ratios exhibit mean-reversion? Figure 10 shows that they do find mean-

reversion in valuation ratios;

2) Is the mean-reversion driven by increases in prices? Figure 11 shows that mean-

reversion is driven not by price increases rather by company fundamentals, i.e. these

formulaic strategies identify companies with inflated fundamentals.

Fig 10 There is evidence of Mean reversion Earnings to Price Ratios

Fig 11 Reversion in Logged Trailing E-P Ratio: Spread of High vs Middle Quintiles

Source: Richard Sloan, July 2016

So what should investors do to build successful strategies? Prof. Sloan makes the case that

alpha is a zero sum game and to be successful investors should combine detailed

fundamental analysis with quantitative signals. That is, simply picking stocks using PE or PB

measure won’t generate much alpha but by understanding the accounting mechanics of how

these measures are constructed, and whether these are correctly applied, can investors help

build successful (and differentiated) products.


5 July 2016 15

Dynamic Risk Management and Asset Allocation Harold Kim

Founder and Chief Executive Officer, Neo Risk Investment Advisors

Harold’s presentation described his risk-focused approach to investing, i.e. investing with

emphasis on managing risk to acceptable (optimal) levels. He stressed that risk is much more

predictable than returns and we can exploit this feature of the market.

In particular, he focused on target volatility and efficient hedging.

A target volatility consists of systematically adjusting equity exposure to manage risk. It

can be shown to improve the return-risk ratio in many situations. Furthermore, it is possible

to improve on the basic methodology by taking into account the relation between volatility

and expected returns. Typically, expected returns are higher when volatility is low.

However, Harold pointed out that in the Chinese market there have been prolonged

periods of positive correlation between the first and second moment. By capturing such a

relation it is possible to further enhance the risk-adjusted performance of a target volatility

strategy.

Portfolio hedging can be improved by building a risk-based diversifying hedge overlay. In

particular, the overlay is designed to be more effective as risk increases. The idea is that

asymmetric response to positive/negative markets adds value.

Combining target volatility and efficient hedging leads to a dramatic improvement in returns

with lower risk and drawdown.


5 July 2016 16

Quant Equity Trading in China Hua He

Professor of Financial Practice, CKGSB; Founder and Chief Executive Officer, Shanghai Nine Martingale Investment Management, LP

Grace Xing Hu

Assistant Professor, School of Economics and Finance, The University of Hong Kong

Grace illustrated the results of her thorough analysis based on a novel dataset of stock

returns for the Chinese market. She found a significant size effect (Fig 12) but no robust value

effect in the Chinese stock market. The evidence suggests, both in time series regression and

Fama-Macbeth cross-sectional tests, that the small minus large factor appears to be the

strongest factor in explaining the cross-section of Chinese stock returns.

Her results contradict most of the existing literature which finds instead a significant value

effect. She showed that this difference comes from the extreme values in a few months

during the early years of the sample.

Fig 12 Monthly excess returns of 10 size-sorted portfolios

Source: Chen, Hu, Shao and Wang, “Fama-French in China”, July 2016

Hua built on Grace’s results to explain his factor-based stock selection approach for the

China-A market. He went through the valuation, technical and other style factors used by his

firm. His main conclusions were:

1) Quant equity strategies work well in China, generating much larger alpha than in

other markets. This may be due to the high participation of individual investors.

2) Hedging is challenging under the current situation.

3) There is significant demand for market neutral strategies from high net worth

individuals in China.


5 July 2016 17

Multi-Horizon Modelling: Balancing Flexibility, Stability and Decay Mark Nebelung

Co-Head of Global Systematic Solutions, Principal Global Equities

How does one build a model that incorporates factors with different horizons, payoffs across

sectors and time varying performance? Mark Nebelung from Principal Global Equities shared

his firm’s experience in building such a model.

The talk and foundations of Mark and team’s process centred around three main challenges:

Decay, Stability and Flexibility.

The alpha achieved from different factors in a factor library will be realised over different

forward horizons. This is widely known however Mark additionally explored the significant

time varying nature of these payoffs. Returns will also vary by sector and can be far from

symmetric across the factor.

Fig 13 The decay profile of factors varies significantly across time

Source: Principal Global Equities, July, 2016

Mark showed that there were clear benefits to the risk/return profile of using a dynamic multi-

factor approach with multiple horizons.

He explored the benefits of:

Modelling horizons from 1 month through 12 months independently and then combining

Dynamic factor selection within each sector for each horizon

Modelling the long and short sides separately

Using Longer horizon models for strategic positioning and shorter horizon models for

tactical positioning

This approach involves a significant amount of computing power and creates a large number

of models that then need to be effectively combined. Mark suggests using Machine Learning

techniques to overcome this problem and thinks finance will become more comfortable with

these methods in coming years. Mark’s recommendation is to use random forest models as

these are less prone to over-fitting.


5 July 2016 18

Stalking the herd Giuliano De Rossi

Head of European Quant Research, Macquarie

Academics have documented that institutional investors tend to follow the herd, i.e. refrain

from buying a stock they regard as attractive if it is being sold by a number of other market

participants. This phenomenon is often taken as evidence that portfolio managers prefer to be

wrong along with everybody else rather than taking the risk of being right, or wrong, alone.

We show that herding has strong implications for performance in the long term: Stocks that

are bought by the herd tend to overshoot and subsequently underperform. Fig 14 illustrates

this result by displaying the returns of two baskets: The top and bottom third of global stocks

sorted by herding. For long holding periods (e.g. 12 months) the reversal effect offsets the

initial continuation one and results in a negative return spread between buys and sells.

Fig 14 Performance of stocks bought / sold by the herd


In addition, herding tends to concentrate on stocks that look unattractive from a quant

perspective (i.e. expensive, illiquid, low quality) - a result likely due to behavioural reasons.

Using data on global institutional holdings we investigate whether it is possible to exploit

these findings to enhance our stock selection factors by taking into account the amount of

herd-buying or selling that characterises a stock. The main conclusions are:

1) Stocks that have been sold by mutual funds significantly outperform those that have

been bought over horizons of 9 to 12 months

2) It is possible to enhance the performance of most traditional quant factors by double

sorting on herding and quant scores (Fig 15)

0.00%

0.25%

0.50%

0.75%

1.00%

1 3 12

Holding period

1-month Herd signal

Buys Sells


5 July 2016 19

Fig 15 Comparison of single vs. double sorting, monthly return spread


0.00%

0.25%

0.50%

0.75%

1.00%

Factor spread Factor + Herd spread

Earnings momentum

Long Short

0.00%

0.25%

0.50%

0.75%

1.00%


6-month price momentum

Long Short

0.00%

0.25%

0.50%

0.75%

1.00%


12-month price momentum

Long Short

0.00%

0.25%

0.50%

0.75%


Size

Long Short


5 July 2016 20

A Wealth Management Perspective on Factor Premia and the Value of Downside Protection Stefano Cavaglia

Head of Investment Research

Stefano’s latest work focuses on the benefits of factor premia to the average investor. “Joe

Coal-Minder” was introduced as the average investor and consumer of asset management

products. Stefano then called out the misalignment that exists between the typical investment

management objective, “Generate Alpha” and what is really important to Joe, “Capital

Protection”.

Stefano explored how exposure to factor premia can help Joe’s real problem focussing on

three key questions.

How is the distribution of terminal wealth altered by a premia overlay?

What if the returns to premia strategies are halved?

How do Premia compare to TAA skill?

How bumpy is the path to terminal wealth outcomes?

What is the value of downside protection?

How do I benchmark different value functions?

How much capital should I allocate to factor premia?

In summary, a factor premia overlay can add significant value to vanilla market exposure and

this holds across all global markets. Even when the premia is halved it still has significant

benefits and is comparable to a TAA approach with assumed skill.

Fig 16 Impact (returns) of Risk Premia on Terminal Wealth

Source: Findex July, 2016

Stefano shows how the counter-cyclical properties of factor premia are just as important as

their potential to provide alternative sources of returns. ie the risk premia provide a hedge

and limit drawdowns in tough markets.

From an Asset Allocation perspective Stefano suggests an allocation of 15% or more to

premia for every dollar in equities.

Stefano’s implementation of the premia strategy is designed quite simply and he encouraged

the audience to build on the concept and enhance further the returns for the average

consumer of our products.

Percentile Return

Expectation

Significant improvement in terminal value


5 July 2016 21

Option: Return Predictability Presented by

Jie (Jay) Cao: Chinese University of Hong Kong

Xintong (Eunice) Zhan: Erasmus University Rotterdam & Rayliant Global Advisors

In this paper the authors present their research looking at the mispricing available in the

options market. Jie and Xintong highlight how crowded the research on vanilla equities is and

that there is limited research / more opportunity in the options market.

The paper studies whether the cross-section of equity options returns can be explained by

various characteristics of the underlying stocks. The authors put forth that selling options on

average contains an expected return premium, and such premium varies across stocks and

can be predicted by several stock market characteristics

The authors create monthly buy-and-hold strategy using delta–neutral call writing.

This eliminates the option’s exposure to underlying stock price movement.

The monthly buy-and-hold return is practical for trading

Eight out of the twelve stock market characteristics that are examined independently predict

delta-hedged option gains in the cross-section.

Positively: Size/Ret(-1,0)/Ret(-12,-2)/Profitability

Negatively: Cash-to-asset/New issue/Analyst dispersion/Idio. Volatility

Fig 17 Rolling return of strategies across different factors

Source: Chinese University of Hong Kong, July, 2016

The results cannot be explained by common risk factors, underlying stock price movement,

volatility mispricing or risks, or option liquidity, etc.

The authors also examine the impact of transaction costs. Whilst they do decrease the profits

of the trading strategies there is still significant alpha to be gained.

– Ln(ME) – RET(-1,0)

– RET(-12,-2) + CH

-4

-2

0

2

4

6

8

10

12

-6

-4

-2

0

2

4

6

8

10

-6

-4

-2

0

2

4

6

8

10

12

-6

-4

-2

0

2

4

6

8

10

1996-2000 2001-2004 2005-2008 2009-2012 1996-2000 2001-2004 2005-2008 2009-2012

1996-2000 2001-2004 2005-2008 2009-2012 1996-2000 2001-2004 2005-2008 2009-2012


5 July 2016 22

Trading for Status Wenxi Jiang

Assistant Professor, The Chinese University of Hong Kong

Wenxi focuses on the Chinese equity market and some unique data sources in this

presentation to highlight how a ‘Keeping up with the Joneses’ mentality leads to time-varying

demand and trading in local stocks.

One fundamental question in financial economics is why individual investors trade so much.

There are three sets of stylized facts that characterise retail behaviour:

Excessive trading, and more trading associated with worse performance (e.g. Odean '99)

Holding and trading are biased towards small local stocks or companies they work at (e.g.

Grinblatt and Keloharju '01)

The literature has shown that several behavioural biases are key drivers.

Such as overconfidence, sensation seeking, familiarity bias and extrapolative expectations.

The Chinese market is ideal for this study given the separation across classes and the

changing nature of these classes that’s being witnessed over the last 20 years. China’s stock

markets are also largely driven by domestic retail investors whose portfolios are strongly

locally biased. Wenxi focuses on smaller companies as these are likely to be more retail

driven and uses unique datasets such as stock message boards (with IP tracking) and

brokerage house trading during the period of 1998-2012 to run the analysis.

Fig 18 Turnover Gap on GDP Per Capita Proxies at City Level

Source: Chinese University of Hong Kong, July, 2016

Wenxi shows using a simple model that Keeping-up-with-the-Joneses preferences can also

lead to excessive trading that varies with past returns. When local stocks are doing well, the

wealth of the peer group is high and so is the marginal utility of wealth of investors with status

concerns. This leads to a greater demand for the local risky assets.

Low market values of the local stocks reduce the need for this status generated risk-demand

since there is nothing to keep up with. Households with status concerns as a result display

trend chasing behaviour. They trade with market makers or arbitrageurs who do not have

status preferences.

Wenxi shows that share turnover increases with the intensity of status. Since this trading is

driven by a desire to keep up and hedge status, it is associated with poor expected returns.

As a result, status concerns can reinforce and amplify behavioural biases behind the retail

trading puzzles


5 July 2016 23

Important disclosures:

Recommendation definitions

Macquarie - Australia/New Zealand Outperform – return >3% in excess of benchmark return Neutral – return within 3% of benchmark return Underperform – return >3% below benchmark return Benchmark return is determined by long term nominal GDP growth plus 12 month forward market dividend yield

Macquarie – Asia/Europe Outperform – expected return >+10% Neutral – expected return from -10% to +10% Underperform – expected return <-10%

Macquarie – South Africa Outperform – expected return >+10% Neutral – expected return from -10% to +10% Underperform – expected return <-10%

Macquarie - Canada

Outperform – return >5% in excess of benchmark return Neutral – return within 5% of benchmark return Underperform – return >5% below benchmark return

Macquarie - USA Outperform (Buy) – return >5% in excess of Russell 3000 index return Neutral (Hold) – return within 5% of Russell 3000 index return Underperform (Sell)– return >5% below Russell 3000 index return

Volatility index definition*

This is calculated from the volatility of historical price movements. Very high–highest risk – Stock should be

expected to move up or down 60–100% in a year – investors should be aware this stock is highly speculative. High – stock should be expected to move up or down at least 40–60% in a year – investors should be aware this stock could be speculative. Medium – stock should be expected to move up or down at least 30–40% in a year. Low–medium – stock should be expected to move up or down at least 25–30% in a year. Low – stock should be expected to move up or down at least 15–25% in a year. * Applicable to Asia/Australian/NZ/Canada stocks only

Recommendations – 12 months Note: Quant recommendations may differ from Fundamental Analyst recommendations

Financial definitions

All "Adjusted" data items have had the following adjustments made: Added back: goodwill amortisation, provision for catastrophe reserves, IFRS derivatives & hedging, IFRS impairments & IFRS interest expense Excluded: non recurring items, asset revals, property revals, appraisal value uplift, preference dividends & minority interests EPS = adjusted net profit / efpowa* ROA = adjusted ebit / average total assets ROA Banks/Insurance = adjusted net profit /average total assets ROE = adjusted net profit / average shareholders funds Gross cashflow = adjusted net profit + depreciation *equivalent fully paid ordinary weighted average number of shares All Reported numbers for Australian/NZ listed stocks are modelled under IFRS (International Financial Reporting Standards).

Recommendation proportions – For quarter ending 31 March 2016

AU/NZ Asia RSA USA CA EUR Outperform 50.34% 59.09% 46.67% 44.76% 60.66% 46.12% (for global coverage by Macquarie, 3.72% of stocks followed are investment banking clients)

Neutral 34.14% 25.66% 32.00% 49.90% 30.33% 35.10% (for global coverage by Macquarie, 4.79% of stocks followed are investment banking clients)

Underperform 15.52% 15.26% 21.33% 5.33% 9.02% 18.78% (for global coverage by Macquarie, 2.31% of stocks followed are investment banking clients)

Company-specific disclosures: Important disclosure information regarding the subject companies covered in this report is available at www.macquarie.com/research/disclosures.

Analyst certification: We hereby certify that all of the views expressed in this report accurately reflect our personal views about the subject company or companies and its or their securities. We also certify that no part of our compensation was, is or will be, directly or indirectly, related to the specific recommendations or views expressed in this report. The Analysts responsible for preparing this report receive compensation from Macquarie that is based upon various factors including Macquarie Group Limited (MGL) total revenues, a portion of which are generated by Macquarie Group’s Investment Banking activities. General disclosure: This research has been issued by Macquarie Securities (Australia) Limited ABN 58 002 832 126, AFSL 238947, a Participant of the ASX and Chi-X Australia Pty Limited. This research is distributed in Australia by Macquarie Wealth Management, a division of Macquarie Equities Limited ABN 41 002 574 923 AFSL 237504 ("MEL"), a Participant of the ASX, and in New Zealand by Macquarie Equities New Zealand Limited (“MENZ”) an NZX Firm. Macquarie Private Wealth’s services in New Zealand are provided by MENZ. Macquarie Bank Limited (ABN 46 008 583 542, AFSL No. 237502) (“MBL”) is a company incorporated in Australia and authorised under the Banking Act 1959 (Australia) to conduct banking business in Australia. None of MBL, MGL or MENZ is registered as a bank in New Zealand by the Reserve Bank of New Zealand under the Reserve Bank of New Zealand Act 1989. Apart from Macquarie Bank Limited ABN 46 008 583 542 (MBL), any MGL subsidiary noted in this research, , is not an authorised deposit-taking institution for the purposes of the Banking Act 1959 (Australia) and that subsidiary’s obligations do not represent deposits or other liabilities of MBL. MBL does not guarantee or otherwise provide assurance in respect of the obligations of that subsidiary, unless noted otherwise. This research contains general advice and does not take account of your objectives, financial situation or needs. Before acting on this general advice, you should consider the appropriateness of the advice having regard to your situation. We recommend you obtain financial, legal and taxation advice before making any financial investment decision. This research has been prepared for the use of the clients of the Macquarie Group and must not be copied, either in whole or in part, or distributed to any other person. If you are not the intended recipient, you must not use or disclose this research in any way. If you received it in error, please tell us immediately by return e-mail and delete the document. We do not guarantee the integrity of any e-mails or attached files and are not responsible for any changes made to them by any other person. Nothing in this research shall be construed as a solicitation to buy or sell any security or product, or to engage in or refrain from engaging in any transaction. This research is based on information obtained from sources believed to be reliable, but the Macquarie Group does not make any representation or warranty that it is accurate, complete or up to date. We accept no obligation to correct or update the information or opinions in it. Opinions expressed are subject to change without notice. The Macquarie Group accepts no liability whatsoever for any direct, indirect, consequential or other loss arising from any use of this research and/or further communication in relation to this research. The Macquarie Group produces a variety of research products, recommendations contained in one type of research product may differ from recommendations contained in other types of research. The Macquarie Group has established and implemented a conflicts policy at group level, which may be revised and updated from time to time, pursuant to regulatory requirements; which sets out how we must seek to identify and manage all material conflicts of interest. The Macquarie Group, its officers and employees may have conflicting roles in the financial products referred to in this research and, as such, may effect transactions which are not consistent with the recommendations (if any) in this research. The Macquarie Group may receive fees, brokerage or commissions for acting in those capacities and the reader should assume that this is the case. The Macquarie Group‘s employees or officers may provide oral or written opinions to its clients which are contrary to the opinions expressed in this research. Important disclosure information regarding the subject companies covered in this report is available at www.macquarie.com/disclosures © Macquarie Group

This publication was disseminated on 05 July 2016 at 05:05 UTC.

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