Seasonality

First-Half Month Cash-Flow News and Momentum in Stocks

24.September 2020

Stock prices react to the new information that investors continually receive from many sources. There are some major events, which are commonly connected with a new piece of information and subsequent reactions of investors. For example, quarterly earnings-announcements are the cause of the post-earnings-announcement drift or PEAD. According to the PEAD, prices tend to continue to drift up (down) after positive (negative) news. But news related to quarterly announcements is not the only important information. A novel research paper written by the Hong and Yu explores implications of the month-end reporting, analyst revisions and management guidance that are coming to market usually in the first half of each month and are also connected with drifts that offer practitioners profitable opportunities.

Authors: Claire Yurong Hong and Jialin Yu

Title: Month-End Reporting, Cash-Flow News, and Asset Pricing

Continue reading

Pre-Election Drift – Video

28.August 2020

The presidential campaign is becoming hotter as we are moving closer to this year’s election. But we still have enough time to dig deeper into data about the past elections and prepare for autumn. Therefore, we have prepared a short video recapitulation of our paper on the pre-election drift.

Continue reading

Secular Decline in Yields around FOMC Meetings

24.April 2020

Federal Open Market Committee meetings (aka FED meetings) have a significant influence on the number of different assets (see for example our article related to drift in equities during FED meetings). The main channel which FED uses to influence the US economy is the level of short term interest rates. Therefore, it’s not a surprise that FED meetings have influence also on long-term interest rates. But just how big? Bigger than most people think. We are presenting one interesting research paper written by Sebastian Hillenbrand, which shows that the whole secular decline in equity yields and long-term interest rates since 1980 was realized entirely in a 3-day window around FOMC meetings. Now, that’s called the influence …

Author: Hillenbrand

Title: The Secular Decline in Long-Term Yields around FOMC Meetings

Continue reading

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.

Continue reading

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

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2382197

Abstract:

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."


Are you looking for more strategies to read about? Check http://quantpedia.com/Screener

Do you want to see a performance of trading systems we described? Check http://quantpedia.com/Chart/Performance

Do you want to know more about us? Check http://quantpedia.com/Home/About


Follow us on:

Facebook: https://www.facebook.com/quantpedia/

Twitter: https://twitter.com/quantpedia


 

Continue reading

Case Study: Quantpedia’s Composite Seasonal / Calendar Strategy

26.April 2019

Despite the fact that the economic theory states that financial markets are efficient and investors are rational, a large amount of research is about anomalies, where the result is different from the theoretical expectation. At Quantpedia, we deal with anomalies in the financial markets and we have identified more than 500 attractive trading systems together with hundreds of related academic papers.

This article should be a case study of some strategies that are listed in our Screener, with an aim to present a possible usage of strategies in our database. Moreover, we have extended the backtesting period and we show that the strategies are still working and have not diminished. This blog also should serve as a case study how to use the Quantpedia’s database itself; therefore the choice of strategies was not obviously random and strategies were filtered by given criteria, however, every strategy is listed in the “free“ section, and therefore no subscription is needed.

Continue reading

Subscribe for Newsletter

Be first to know, when we publish new content


logo
The Encyclopedia of Quantitative Trading Strategies

Log in