New strategies:
#615 – Machine Learning and Mutual Fund Characteristics
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Very complex strategy
Backtest period: 1980-2018
Indicative performance: 6.32%
Estimated volatility: 12.85%
Source paper:
Bin Li, Alberto Rossi: Selecting Mutual Funds From the Stocks They Hold: a Machine Learning Approach
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737667
Abstract:
We select mutual funds in real time by combining individual fund holdings and a large number (94) of stock characteristics to compute fund-level characteristics on the basis of the stocks they hold. We show that, first, the majority of funds are largely exposed|both positively and negatively|to approximately 40-50 characteristics. Second, fund performance is non-linearly related to fund characteristics and there are significant degrees of interaction between different fund characteristics and fund performance. Third, when we predict fund performance, these nonlinearities and interactions prove important as machine learning methods such as Boosted Regression Trees (BRT) outperform significantly standard linear frameworks and the BRT-generated forecasts encompass the ones generated by the predictors of mutual fund performance that have been proposed in the literature so far. Fourth, while in our setting BRT outperform the LASSO, elastic nets, random forests, and neural networks with 1 through 5 hidden layers, these other machine learning methods deliver good performance and they all outperform ordinary least squares models. Finally, while we detect significant predictability using machine learning methods, the fund characteristics that matter the most in predicting fund returns and the functional relation between fund characteristics and fund performance are time-varying
#616 – Output Gap Predicts FX Returns
Period of rebalancing: Monthly
Markets traded: currencies
Instruments used for trading: CFDs, forwards, futures
Complexity: Moderately complex strategy
Backtest period: 1999-2016
Indicative performance: 4.92%
Estimated volatility: 6.83%
Source paper:
Riccardo Colacito, Steven J. Riddiough, and Lucio Sarno: Business Cycles and Currency Returns
https://www.nber.org/system/files/working_papers/w26299/w26299.pdf
Abstract:
We find a strong link between currency excess returns and the relative strength of the business cycle. Buying currencies of strong economies and selling currencies of weak economies generates high returns both in the cross section and time series of countries. These returns stem primarily from spot exchange rate predictability, are uncorrelated with common currency investment strategies, and cannot be understood using traditional currency risk factors in either unconditional or conditional asset pricing tests. We also show that a business cycle factor implied by our results is priced in a broad currency cross section.
#617 – Earnings Announcement Beta
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1982-2017
Indicative performance: 7.7%
Estimated volatility: 15.21%
Source paper:
Jingjing Chen, Linda H. Chen, George J. Jiang: Earnings Announcement Beta and Accrual of Risk Premium
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3804562
Abstract:
Earnings announcements present a clear risk to investors and, under rational asset pricing theory, such risk should be consistently priced in stocks. However, we find that stocks with high earnings announcement risk earn significantly higher returns only during months when firms have earnings or M&A announcements. Moreover, the higher returns are realized mostly around the date of announcements. The findings seem to suggest that the risk premium is accrued concurrently when investors adjust stock valuation in response to significant information events. We provide additional evidence to substantiate the conjecture based on the effects of information updates and investor information consumption.
#618 – Bond Yield Changes and the Cross Section of Equity Indices
Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: ETFs, futures
Complexity: Simple strategy
Backtest period: 1900-2019
Indicative performance: 7.83%
Estimated volatility: 15.31%
Source paper:
Adam Zaremba, Nusret Cakici, Robert Bianchi, Huaigang Long: Bond Yield Changes and the Cross-Section of Global Equity Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3756047
Abstract:
We document a new cross-sectional anomaly that links international government bond and equity markets. Using a unique long-run dataset of 61 countries for the years 1900–2019, we demonstrate that past bond yield changes predict future stock index returns in the cross-section. The quintile of countries with the largest decline (or smallest increase) in government bond yields outperforms the quintile of countries with the smallest decline (or largest increase) by 0.63% per month. Our findings support the behavioral roots of this effect, suggesting that investors underreact to yield changes, and slow-moving capital prevents arbitrageurs from eliminating the anomaly. Global investors can employ this bond yield change effect to enhance international asset allocation decisions.
#619 – Trend Factor in Corporate Bonds
Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Moderately complex strategy
Backtest period: 1973-2019
Indicative performance: 16.76%
Estimated volatility: 7.58%
Source paper:
Guo, Xu and Lin, Hai and Wu, Chunchi and Zhou, Guofu: Extracting Information from Corporate Bond Yields
https://ssrn.com/abstract=3740807
Abstract:
We document strong evidence of cross-sectional predictability of corporate bond returns based on a set of yield predictors that capture the information in the yields of past 1, 3, 6, 12, 24, 36 and 48 months. Return predictability is economically and statistically significant, and is robust to various controls. The uncovered predictability presents the most pronounced anomaly in the corporate bond literature that challenges rational pricing models.
New research papers related to existing strategies:
#33 – Post-Earnings Announcement Effect
Akbas, Ferhat and Ay, Lezgin and Koch, Paul D., Leverage Constraints, Arbitrage Capital, and Investor Under-reaction
https://ssrn.com/abstract=3792433
Abstract:
We analyze a hand-collected sample of earnings announcements over the period, 1934 – 1975, when the Fed changed margin requirements 22 times. We find that higher margin requirements are associated with greater under-reaction to earnings surprises. These results are stronger when investors face greater arbitrage risk or limited attention. They are robust when we control for macroeconomic conditions, financial market conditions, sentiment, or risk, and when we analyze several indirect measures of leverage constraints using more recent data. Our findings suggest that leverage constraints limit capital available to arbitrageurs, and thereby prevent the timely incorporation of earnings information into prices.
#33 – Post-Earnings Announcement Effect
Akbas, Ferhat and Ay, Lezgin and Koch, Paul D., Leverage Constraints, Arbitrage Capital, and Investor Under-reaction
https://ssrn.com/abstract=3792433
Abstract:
We analyze a hand-collected sample of earnings announcements over the period, 1934 – 1975, when the Fed changed margin requirements 22 times. We find that higher margin requirements are associated with greater under-reaction to earnings surprises. These results are stronger when investors face greater arbitrage risk or limited attention. They are robust when we control for macroeconomic conditions, financial market conditions, sentiment, or risk, and when we analyze several indirect measures of leverage constraints using more recent data. Our findings suggest that leverage constraints limit capital available to arbitrageurs, and thereby prevent the timely incorporation of earnings information into prices.
#460 – ESG Level Factor Investing Strategy
Chen, Qian and Liu, Xiao-Yang, Quantifying ESG Alpha in Scholar Big Data: An Automated Machine Learning Approach
https://ssrn.com/abstract=3806840
Abstract:
ESG (Environmental, social and governance) alpha strategy that makes sustainable investment has gained popularity among investors. The ESG fields of study in scholar big data is a valuable alternative data that reflects a company’s long-term ESG commitment. However, it is considered a difficulty to quantitatively measure a company’s ESG premium and its impact to the company’s stock price using scholar big data. In this paper, we utilize ESG scholar data as alternative data to develop an automatic trading strategy and propose a practical machine learning approach to quantify the ESG premium of a company and capture the ESG alpha. First, we construct our ESG investment universe and apply feature engineering on the companies’ ESG scholar data from the Microsoft Academic Graph database. Then, we train six complementary machine learning models using a combination of financial indicators and ESG scholar data features and employ an ensemble method to predict stock prices and automatically set up portfolio allocation. Finally, we manage our portfolio, trade and rebalance the portfolio allocation monthly using predicted stock prices. We backtest our ESG alpha strategy and compare its performance with benchmarks. The proposed ESG alpha strategy achieves a cumulative return of 2,154.4% during the backtesting period of ten years, which significantly outperforms the NASDAQ-100 index’s 397.4% and S&P 500’s 226.9%. The traditional financial indicators results in only 1,443.7%, thus our scholar data-based ESG alpha strategy is better at capturing ESG premium than traditional financial indicators.
#5 – FX Carry Trade
Nissinen, Juuso and Suominen, Matti and Ferreira Filipe, Sara, Currency Carry Trades and Global Funding Risk
https://ssrn.com/abstract=3795484
Abstract:
We measure funding constraints in international currency markets by deviations in the covered interest rate parity. Our measure of funding risk is the standard deviation of the magnitude of the funding constraints. This funding risk measure appears to be driven by conditions in the financial sector in the low interest rate, so called carry trade short countries, oil price volatility, as well as by the actions of the main central banks. Although funding risk has been present throughout our sample, it becomes only relevant in currency carry trading after 2008, suggesting that investors’ funding constraints start binding at that time. We document evidence that since 2008 funding risk has affected the magnitude of currency carry trading activity, carry trade returns, correlation between carry long and short currencies, relative equity returns in carry trade long vs. short countries, and the economies of carry trade long countries measured through changes in industrial production. We develop a theory of currency markets under funding constraints that has several testable implications. For instance, as funding constraints start to bind, our theory predicts that both the investment and funding currencies drop relative to a safe asset. This result is observable also in our empirical analysis, when we proxy for the safe asset with gold. In line with theory, funding risk forecasts currency crashes in the carry trade long and short countries.
#481 – Holding Artificial VIX in a Portfolio
Hirsa, Ali and Hadji Misheva, Branka and Osterrieder, Joerg and Cao, Wenxin and Fu, Yiwen and Sun, Hanze and Wong, Kin Wai, THE VIX INDEX UNDER SCRUTINY OF MACHINE LEARNING TECHNIQUES AND NEURAL NETWORKS
https://ssrn.com/abstract=3796351
Abstract:
The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the market’s expected volatility on the S&P 500 Index, calculated and published by the Chicago Board Options Exchange (CBOE). It is also often referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the S&P 500 index over the following 30 days, based on options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE’s Volatility Index is prone to inadvertent and deliberate errors because it is weighted average of out-of-the-money calls and puts which could be illiquid. Many claims of market manipulation have been brought up against VIX in recent years. This paper discusses several approaches to replicate the VIX index as well as VIX futures by using a subset of relevant options as well as neural networks that are trained to automatically learn the underlying formula. Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, we will show that a small number of options is sufficient to replicate the VIX index. Once we are able to actually replicate the VIX using a small number of S&P options we will be able to exploit potential arbitrage opportunities between the VIX index and its underlying derivatives. The results are supposed to help investors to better understand the options market, and more importantly, to give guidance to the US regulators and CBOE that have been investigating those manipulation claims for several years.
And two interesting free blog posts have been published during last 2 weeks:
Thanks to quantitative easing, we see record-low interest rates. While yields for short to intermediate maturities in the US are lower than the inflation but still positive, other developed markets such as Japan or European countries even have bond yields negative. Still, it does not implicate that investors have withdrawn from the fixed income markets. Both individual and institutional investors still participate in bond trading. However, the critical question is how these conditions influence the investors. Does their behavior change? Do they reach for yield and prefer riskier bonds in the search for (positive) real yields? In this blog post, we present three novel research papers that offer insights into this topic.
Market Sentiment and an Overnight Anomaly
Various research papers show that market sentiment, also called investor sentiment, plays a role in market returns. Market sentiment refers to the general mood on the financial markets and investors’ overall tendency to trade. The mood on the market is divided into two main types, bullish and bearish. Naturally, rising prices indicate bullish sentiment. On the other hand, falling prices indicate bearish sentiment. This paper shows various ways to measure market sentiment and its influence on returns.
Additionally, we take a look at an overnight anomaly in combination with three market sentiment indicators. We analyse the Brain Market sentiment indicator in addition to VIX and the short-term trend in SPY ETF. Our aim is not to build a trading system. Instead, it is to analyze financial markets behaviour. Overall the transaction costs of this kind of strategy would be high. However, more appropriate than using this system on its own would be to use it as an overlay when deciding when to make trades.
Plus, the following five trading strategies have been backtested in QuantConnect in the previous two weeks:
#599 – Barbell Strategy
#607 – Equity Momentum Spillover to Currencies
#611 – Idiosyncratic Volatility in China
#612 – Abnormal Turnover in China
#613 – Seasonal Difference in China
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