Quantpedia Premium Update – 15th October 2021

New strategies:

#674 –Inflation Volatility Risk and Corporate Bonds

Period of rebalancing: Monthly
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Complex strategy
Backtest period: 2002-2019
Indicative performance: 6.42%
Estimated volatility: 13.47%

Source paper:

Luis Ceballos: Inflation Volatility Risk and the Cross-section of Corporate Bond Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3883556
Abstract:
As corporate bonds are primarily denominated in nominal terms, inflation uncertainty arises as a relevant source of risk. This paper analyzes the relevance of inflation volatility risk as an additional factor in predicting the cross-section of corporate bond returns. I find a negative and significant inflation volatility risk premium (IVRP) obtained from the di erence between high inflation and low inflation beta portfolios. Further, common risk factors in the equity and corporate bond markets do not explain the IVRP, it responds to ex-post inflation risk and is partially explained by market risk and monetary policy shocks. Lastly, I show that the IVRP is associated with rms that incur in debt maturity management to mitigate re nancing risks.

#675 – Investor Ambiguity in Equities

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Simple strategy
Backtest period: 1970-2019
Indicative performance: 5.03%
Estimated volatility: 10.34%

Source paper:

Lawrence Hsiao: Ambiguity, Investor Disagreement, and Expected Stock Returns
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3880731
Abstract:
I set up a model in which two types of ambiguity-averse traders disagree on how to interpret a public signal. When traders first observe contradicting interpretations of the signal, they don’t know whether to attribute the clash of opinions to different
information processing or to information asymmetry, and thus treat the other type’s interpretation as an ambiguous signal. This ambiguity decreases over time as traders gradually learn or form beliefs about the other type’s interpretation. The model
predicts a positive relation between investor disagreement (ID) and expected stock return, given that the public signal is very imprecise. Also, ID can be measured using the negative correlation coefficient between trading volume and absolute price change. I find that stocks in the highest ID decile outperform stocks in the lowest ID decile by 8.7 percent annually, adjusted for exposures to the market return as well as size, value, momentum, and liquidity factors. In addition, stocks with high ID prior to the earnings announcement experience signi cantly higher returns around the earnings announcement compared to stocks with low ID.

#676 – Short-Term Reversal and High Uncertainty Periods

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: stocks
Complexity: Moderately complex strategy
Backtest period: 1985-2019
Indicative performance: 17.36%
Estimated volatility: 20.61%

Source paper:

Chui, Andy Chun Wai, Economic Policy Uncertainty and Short-term Reversals
https://ssrn.com/abstract=3924771
Abstract:
This study finds that short-term reversals become more profound in the current month when economic policy uncertainty is larger in the prior month. There is evidence that the economic policy uncertainty influences return reversals through the liquidity channel. Short-term reversal profits are also positively related to the VIX index, the Baker-Wurgler (2007) investor sentiment index, and the Aruoba-Diebold-Scotti (2009) business conditions index in the prior month. Though, the predictability of the latter two indexes is less robust. However, adding these indexes and other variables does not weaken the relationship between economic policy uncertainty and return reversals.

#677 – Betting Against Uncertainty Beta in US Hedge Funds

Period of rebalancing: Monthly
Markets traded: equities
Instruments used for trading: funds
Complexity: Moderately complex strategy
Backtest period: 1994-2017
Indicative performance: 8.89%
Estimated volatility: 14.24%

Source paper:

Liang, Bing and Wang, Songtao and Zhou, Chunyang, Economic Policy Uncertainty and Hedge Fund Returns
https://ssrn.com/abstract=3758012
Abstract:
In this paper, we study if the risk associated with innovations in economic policy uncertainty (EPU), that is, EPU risk, is priced in the cross section of hedge fund returns. Based on decile portfolios sorted on the EPU beta, we show that EPU risk commands a significantly negative premium of -0.49% per month. Conventional risk factors cannot explain the return pattern associated with the spread in EPU betas, and our findings are robust to controls for share restrictions, exposure to other risks, and the illiquidity of assets under management and to the exclusion of financial crises. In addition, we show that 12.5% of hedge funds have EPU timing ability and that an investment strategy long in top-ranked EPU timing funds and short in bottom-ranked EPU timing funds delivers a significantly positive risk-adjusted return.

#678 – Systematic Trading of Municipal

Period of rebalancing: Daily
Markets traded: bonds
Instruments used for trading: bonds
Complexity: Very complex strategy
Backtest period: 2015-2018
Indicative performance: 2.06%
Estimated volatility: 1.38%

Source paper:

Kolm, Petter N. and Purushothaman, Sudar: Systematic Pricing and Trading of Municipal Bonds
https://ssrn.com/abstract=3899133
Abstract:
In this article, the authors propose a systematic approach for pricing and trading municipal bonds, leveraging the feature-rich information available at the individual bond level. Based on the proposed pricing framework, they estimate several models using ridge regression and Kalman filtering. In their empirical work, they show that the models compare favorably in pricing accuracy to those available in the literature. Additionally, the models are able to quickly adapt to changing market conditions. Incorporating the pricing models into relative value trading strategies, the authors demonstrate that the resulting portfolios generate significant excess returns and positive alpha relative to the Vanguard Long-Term Tax-Exempt Fund (VWLTX), one of the largest mutual funds in the municipal space.

New research papers related to existing strategies:

#483 – Forecasting Index Changes in the German DAX Family

Wilkens, Sascha: Predicting Stock Index Changes
https://ssrn.com/abstract=3898108
Abstract:
The addition or deletion of companies to/from a stock index has major consequences, with a change in demand from index-tracking investors and funds constituting the most obvious one. Numerous event studies since the 1980s have evidenced the existence of abnormal returns and trading volumes (‘index effect’) around the announcement and actual change dates for stock indices. This paper presents a model-driven approach to predicting changes in the index membership itself. Index rules are combined with models for the evolution of parameters such as market capitalization that drive a company’s potential inclusion or exclusion. Special attention is paid to the inherent model risk. The 2021 revision of Germany’s blue-chip and mid-cap indices, DAX and MDAX, both in terms of the number of index members and admission criteria, serves as a case study.

#459 – Machine Learning and Currency Carry Strategy

Joseph Zhi Bin Ling, Albert K. Tsui and Zhaoyong Zhang: Trading Macro-Cycles of Foreign Exchange Markets Using Hybrid Models
https://www.mdpi.com/2071-1050/13/17/9820/pdf
Abstract:
Most existing studies on forecasting exchange rates focus on predicting next-period returns. In contrast, this study takes the novel approach of forecasting and trading the longer-term trends (macro-cycles) of exchange rates. It proposes a unique hybrid forecast model consisting of linear regression, multilayer neural network, and combination models embedded with technical trading rules and economic fundamentals to predict the macro-cycles of the selected currencies and investigate the predicative power and market timing ability of the model. The results confirm that the combination model has a significant predictive power and market timing ability, and outperforms the benchmark models in terms of returns. The finding that the government bond yield differentials and CPI differentials are the important factors in exchange rate forecasts further implies that interest rate parity and PPP have strong influence on foreign exchange market participants.

#33 – Post-Earnings Announcement Effect

Hirshleifer, David A. and Hirshleifer, David A. and Peng, Lin and Wang, Qiguang: Social Networks and Market Reactions to Earnings News
https://ssrn.com/abstract=3824022
Abstract:
Using social network data from Facebook, we show that earnings announcements made by firms located in counties with higher investor social network centrality attract more attention from both retail and institutional investors. For such firms, the immediate price and volume reactions to earnings announcements are stronger, and post-announcement drift is weaker. Such firms have lower post-announcement persistence of return volatility but higher persistence in investor attention and trading volume. These effects are stronger for small firms, firms with poor analyst and media coverage, and for stocks with salient returns. Our evidence suggests a dual role of social networks—they facilitate the incorporation of public information into prices, but also trigger persistent excessive trading.

#33 – Post-Earnings Announcement Effect

Friedman, Henry L. and Zeng, Zitong: Retail Investor Trading and Market Reactions to Earnings Announcements
https://ssrn.com/abstract=3817979
Abstract:
This paper uses holdings and outage data from Robinhood and transaction-level data from U.S. exchanges to examine how retail investors affect the pricing of public earnings information. We find that retail trader activity is associated with prices that are more responsive to earnings surprises, and earnings announcements affected by seemingly random retail trading outages experience weaker price responses. These results are concentrated in firms that are smaller and have less robust informational environments. Additional evidence shows that the retail activity is associated with more volatile returns during the earnings announcement window, which can slow the incorporation of public information and contribute to larger bid-ask spreads. Overall, our results suggest that retail investors can facilitate the incorporation of public information into price over the 2-day earnings announcement window despite the potential to increase volatility and impose risk on other market participants.

#309 – Subsidiary – Parent Equity Momentum
#547 – Return Predictability in Firms with Complex Ownership Network

Huang, Wenli and Li, Gang and Zhang, Shaojun: Volatility Information Transfer along the Supply Chain
https://ssrn.com/abstract=3822362
Abstract:
We investigate whether and how information about one stock’s future volatility is transferred to other related stocks along the supply chain. The supply chain setting offers an ideal setting to study the effect of cross-firm volatility information transfer because customers and suppliers are closely related. Earnings announcements can have an impact on the announcing firm’s short-term, long-term, and forward expected volatility. We find that, on average, the announcing firms’ short-term expected volatility decreases substantially after earnings announcements, while the change of their forward expected volatility is close to zero. Regression analysis shows that the change of the announcing firm’s short-term (forward) volatility has a significant effect on the change of its supply chain partner’s short-term (forward) volatility. The effect is economically meaningful and becomes stronger if customers and suppliers are more closely related. Our results yield new insights on the transfer of volatility related information among firms.

And several interesting free blog posts have been published during last 2 weeks:

How to Use Deep Order Flow Imbalance

Order book information is crucial for traders, but it can be complex. With the numbers of stocks listed in stock exchanges, it is impossible to track all the available information for the human mind. Therefore, the order flows could be an interesting dataset for machine learning models. The novel research of Kolm, Turiel and Westray (2021) utilizes deep-learning for high-frequency return forecasts for 115 NASDAQ stocks based on order book information at the most granular level.

The paper has several key contributions. Firstly, it does not forecast one single return but rather a whole vector of returns – a term structure consisting of mid-price return forecasts at a specified horizon. The forecasted term structure provides essential information about the most optimal execution algorithms (or a trading strategy). According to the authors, forecasts have an „accuracy peak“ at two price changes, after which the accuracy declines. Secondly, the paper compares several methods: autoregressive model with exogenous inputs, MLP, LSTM, LSTM-MLP, stacked LSTM, and CNN-LSTM. Therefore, the article could also serve as a horse race across several possible forecasting methods. Lastly, using more traditional statistical approaches, the authors have identified a better forecasting performance in more information-rich stocks. As a result, this novel research could benefit many areas such as high-frequency trading (but trading costs must be considered), optimal execution strategy, or market-making.

Three Simple Tactical FX Hedging Strategies

There are many ways one can lose money when investing, and exchange rates are one of the potential risk factors. Luckily, there are several ways to minimize this type of loss in your portfolio. Systematic FX hedging that uses currency factor strategies is a way of protecting an existing or anticipated position from an unwanted move in an exchange rate. It does not eliminate the risk of loss completely but helps to manage currency exposure better.

Insider Trading: What Happens Behind Closed Doors

Corporate insiders often have insight into a company’s private information, which might help them predict how the shares’ price will move in the coming days. However, laws and regulations are designed to keep them from trading based on this knowledge, as it would be unfair and hurt the company’s other shareholders. This includes the prohibition of insider trading or designing a 10b5-1 plan, which we will discuss in this article. Anyways, knowing about incoming losses or the will to create profits might lead these insiders to different practices that could be questioned. Let’s look at some of the newest research concerning these issues.

What is the Optimal Gold Allocation in a Portfolio?

Ray Dalio, the founder of Bridgewater Associates L.P. and the creator of the All-Weather investment strategy, recommends having some gold in a contemporary environment. He states, “In a world of ongoing pressure for policymakers across the globe to print and spend, zero interest rates, tectonic shifts in where global power lies, and conflict, gold has a unique role in protecting portfolios. It’s wise to hold some gold.” Therefore, one would ask a question, what is the optimal weight of gold in a portfolio?