Market timing

Top Ten Blog Posts on Quantpedia in 2019

29.December 2019

The end of the year is a good time for a short recapitulation. Apart from other things we do (which we will summarize in our next blog in a few days), we have published around 50 short blog posts / recherches of academic papers on this blog during the last year. We want to use this opportunity to summarize 10 of them, which were the most popular (based on Google Analytics tool). Maybe you will be able to find something you have not read yet …

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How to Choose the Best Period for Indicators

3.December 2019

Academic literature recognizes a large set of indicators or factors that are connected with the various assets. These indicators can be utilized in a variety of trading strategies, which means that such indicators are popular among practitioners who seek to invest their funds. Usually, the indicators are connected with some evaluation period.

This paper aims to show some possible approaches to find the optimal evaluation periods of indicators. This is a key question among practitioners and therefore we see it as crucial to shed a light on this topic. Although we are focused on momentum strategies, the information in this paper is widely applicable also in the construction of any other trading strategy where the investor has to decide indicator’s period…

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Calendar / Seasonal Trading and Momentum Factor

29.October 2019

We are continuing in our short series of articles about calendar / seasonal trading. The main focus of this paper is to show that the well-working calendar / seasonal anomalies can be refined. The aim is to find the right factors and find a way how to combine them in a search for profit from the practitioner’s point of view. Based on our previous research, calendar anomalies are profitable, but there is a possible way how to enhance their performance. This can be done by employing momentum strategies. By assigning a weight to assets from a diversified set according to their momentum value, it is possible to find a profitable asset during various global market conditions. Moreover, a trend factor is used to ensure that when market conditions are not favorable, the strategy will not trade. Such addition is a typical approach used for reducing maximal draw-downs. Finally, since this paper is written from the practitioner’s point of view, we are assuming some model transaction costs and examine the strategy in their presence.

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Retail Day Trading is an Uphill Battle

4.September 2019

Do retail day traders have a chance in current financial markets? They often lack proper trading research and infrastructure; they are facing high fees and stiff competition from professionals. But it’s always useful to view actual hard numbers and performance statistics and not just rely on feelings. Luckily, some academic research papers are exploring the question of the performance of retail traders. Chague, De-Losso, and Giovannetti have written the newest one, and as expected, their findings are not very favorable for retail day traders.

Authors: Chague, De-Losso, Giovannetti

Title: Day Trading for a Living?

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The Impact of Crowding on Alternative Risk Premiums

17.May 2019

Related to all factor strategies …

Author: Baltas

Title: The Impact of Crowding in Alternative Risk Premia Investing



Crowding is a major concern for investors in the alternative risk premia space. By focusing on the distinct mechanics of various systematic strategies, we contribute to the discussion with a framework that provides insights on the implications of crowding on subsequent strategy performance. Understanding such implications is key for strategy design, portfolio construction, and performance assessment. Our analysis shows that divergence premia, like momentum, are more likely to underperform following crowded periods. Conversely, convergence premia, like value, show signs of outperformance as they transition into phases of larger investor flows.

Notable quotations from the academic research paper:

"Crowding risk is listed as one of the most important impediments for investing in alternative risk premia. We contribute to this industry debate by exploring the mechanics of the various ARP in the event of investor flows, and study the implications of crowdedness on subsequent performance.

The cornerstone of our methodology is the classification of the ARP strategies into divergence and convergence premia. Divergence premia, like momentum, lack a fundamental anchor and inherently embed a self-reinforcing mechanism (e.g. in momentum, buying outperforming assets, and selling underperforming ones). This lack of a fundamental anchor creates the coordination problem that Stein (2009) describes, which can ultimately have a destabilising effect.

Divergence factor

Conversely, convergence premia, like value, embed a natural anchor (e.g. the valuation spread between undervalued and overvalued assets) that acts as an self-correction mechanism (as undervalued assets are no longer undervalued if overbought). Extending Stein’s (2009) views, such dynamics suggest that investor flows are actually likely to have a stabilising effect for convergence premia.

Convergence premia

In order to test these hypotheses we use the pairwise correlation of factor-adjusted returns of assets in the same peer group (outperforming assets, undervalued assets and so on so forth) as a metric for crowding.

We provide empirical evidence in line with these hypotheses. Divergence premia within equity, commodity and currency markets are more likely to underperform following crowded periods.

All divergence premias

Whereas convergence premia show signs of outperformance as they transition into phases of higher investor flows.

All convergence premias"

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News Implied VIX Since The Year 1890

9.May 2019

We present an interesting academic paper with a methodology that allows estimating VIX (volatility risk) since the year 1890 …

Authors: Manela, Moreira

Title: News Implied Volatility and Disaster Concerns



We construct a text-based measure of uncertainty starting in 1890 using front-page articles of the Wall Street Journal. News implied volatility (NVIX) peaks during stock market crashes, times of policy-related uncertainty, world wars and financial crises. In US post-war data, periods when NVIX is high are followed by periods of above average stock returns, even after controlling for contemporaneous and forward-looking measures of stock market volatility. News coverage related to wars and government policy explains most of the time variation in risk premia our measure identifies. Over the longer 1890-2009 sample that includes the Great Depression and two world wars, high NVIX predicts high future returns in normal times, and rises just before transitions into economic disasters. The evidence is consistent with recent theories emphasizing time variation in rare disaster risk as a source of aggregate asset prices fluctuations.

Notable quotations from the academic research paper:

"This paper aims to quantify this “spirit of the times”, which after the dust settles is forgotten, and only hard data remains to describe the period. Specifically, our goal is to measure people’s perception of uncertainty about the future, and to use this measurement to investigate what types of uncertainty drive aggregate stock market risk premia.

We start from the idea that time-variation in the topics covered by the business press is a good proxy for the evolution of investors’ concerns regarding these topics.

We estimate a news-based measure of uncertainty based on the co-movement between the front-page coverage of the Wall Street Journal and options-implied volatility (VIX). We call this measure News Implied Volatility, or NVIX for short. NVIX has two useful features that allow us to further our understanding of the relationship between uncertainty and expected returns:

(i) it has a long time-series, extending back to the last decade of the nineteen century, covering periods of large economic turmoil, wars, government policy changes, and crises of various sorts;

(ii) its variation is interpretable and provides insight into the origins of risk variation.

The first feature enables us to study how compensation for risks reflected in newspaper coverage has fluctuated over time, and the second feature allows us to identify which kinds of risk were important to investors.

We rely on machine learning techniques to uncover information from this rich and unique text dataset. Specifically, we estimate the relationship between option prices and the frequency of words using Support Vector Regression. The key advantage of this method over Ordinary Least Squares is its ability to deal with a large feature space. We find that NVIX predicts VIX well out-of-sample, with a root mean squared error of 7.48 percentage points (R2 = 0.19). When we replicate our methodology with realized volatility instead of VIX, we find that it works well even as we go decades back in time, suggesting newspaper word-choice is fairly stable over this period.

News Based VIX Index

We study whether fluctuations in NVIX encode information about equity risk premia. We begin by focusing on the post-war period commonly studied in the literature for which high-quality stock market data is available. We find strong evidence that times of greater investor uncertainty are followed by times of above average stock market returns. A one standard deviation increase in NVIX predicts annualized excess returns higher by 3.3 percentage points over the next year and 2.9 percentage points annually over the next two years.

Interpretability, a key feature of the text-based approach, enables us to investigate what type of news drive the ability of NVIX to predict returns. We decompose the text into five categories plausibly related (to a varying degree) to disaster concerns: war, financial intermediation, government policy, stock markets, and natural disasters. We find that a large part of the variation in risk premia is related to wars (53%) and government policy (27%). A substantial part of the time-series variation in risk premia NVIX identifies is driven by concerns tightly related to the type of events discussed in the rare disasters literature."

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