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Too Much Arbitrage Contributes to Overreaction in Post Earnings Announcement Drift


A new financial research paper has been published and is related to all equity long short strategies but mainly to:
#33 - Post-Earnings Announcement Effect

Authors: Xiao Li

Title: Does Too Much Arbitrage Destablize Stock Price? Evidence from Short Selling and Post Earnings Announcement Drift.

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3249254

Abstract:

Stein (2009) suggests that too much arbitrage capital exploiting underreaction can lead to overreaction, pushing price further away from fundamental value. I test this hypothesis by investigating the relation between changes in short interest ratio around earning announcement and the subsequent drift return. There are two main findings in this paper. First, my results suggest that too much arbitrage capital does contribute to overreaction (with a t-statistics around 4 on average). These findings are robust to alternative sample periods or length of the window for drift calculation. Second, contrary to the findings in prior literature that show that short sellers mitigate the magnitude of drift, my results show that almost all of this effect are actually contributed by the observations that are more likely to represent overreaction.

 

Quantpedia

 

To learn more about this paper, view the full article on Quantpedia website:
https://www.quantpedia.com/Blog/Details/too-much-arbitrage-contributes-to-overreaction-in-post-earnings-announcement-dr

About Quantpedia

Quantpedia Mission is to process financial academic research into a more user-friendly form to help anyone who seeks new quantitative trading strategy ideas. Quantpedia team consists of members with strong financial and mathematical background (former quantitative portfolio managers and founders of Quantconferences.com) combined with members with outstanding IT and technical knowledge. Learn more about Quantpedia here: https://quantpedia.com

This article is from Quantpedia and is being posted with Quantpedia’s permission. The views expressed in this article are solely those of the author and/or Quantpedia and IB is not endorsing or recommending any investment or trading discussed in the article. This material is for information only and is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad-based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation by IB to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.


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The Hurst Exponent


By Varun Divakar and Ashish Garg

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In this blog, we will be discussing an important concept in time series analysis: The Hurst exponent. We will learn how to calculate it with the help of an example. First, let us understand what Hurst exponent is.

Hurst Exponent Definition
The Hurst exponent is used as a measure of long-term memory of time series. It relates to the autocorrelations of the time series and the rate at which these decrease as the lag between pairs of values increases.

Hurst Value

Hurst Value is more than 0.5
If the Hurst value is more than 0.5 then it would indicate a persistent time series (roughly translates to a trending market).
Hurst Value is less than 0.5
If the Hurst Value is less than 0.5 then it can be considered as an anti-persistent time series (roughly translates to sideways market).
Hurst Value is 0.5
If the Hurst value is 0.5 then it would indicate a random walk or a market where prediction of future based on past data is not possible.

How To Calculate The Hurst Exponent
To calculate the Exponent, we need to divide the data into different chunks. For example, if you have the return data of BTC/USD for the past 8 days’ data, then you divide it into halves as follows:
Following the example of 8 observations for illustrative purposes only1:
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1Length of the subseries in practical applications is usually much longer and affects the mean and standard deviation of the R/S statistic.

Then we divide the data into 3 different chinks as follows:

  1. Division 1 – one chunk of 8 observations
  2. Division 2 – two chunks of 4 observations each
  3. Division 3 – four chunks of 2 observations each

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After dividing the data into chunks, we perform the following calculations on each chunk:

1. First we calculate the mean of the chunk, with say n observations,

.M = (1/n) [ h(1)+h(2)+...+h(n) ]

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2. Then we calculate the standard deviation (S) of the n observations

s(n) = STD( h(1)+h(2)+...+h(n))

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3. Then we create a mean centered series by subtracting the mean from the observations,

x(1) = h(1) – M
x(2) = h(2) – M
...
x(n) = h(n) - M

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Then we calculate the cumulative deviation by summing up the mean centered values,

Y(1) = x(1)
Y(2) = x(1) + x(2)
...
Y(n) = x(1) + x(2) + ...+ x(n)

5. Next, we calculate the Range (R), which is the difference between the maximum value of the cumulative deviation and the minimum value of the cumulative deviation,

R(n) = MAX[Y(1),Y(2)...Y(n)] - MIN[Y(1),Y(2)...Y(n)]

6. And finally, we compute the ratio of the range R to the standard deviation S. This also known as the rescaled range.

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Once we have the rescaled range for all the chunks, we compute the mean of each Division and note it along with the number of samples in each chunk of that Division as shown.

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Next, we calculate the logarithmic values for the size of each region and for each region’s rescaled range.

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The Hurst exponent ‘H’ is nothing but the slope of the plot of each range’s log(R/S) versus each range’s log(size). Here log(R/S) is the dependent or the y variable and log(size) is the independent or the x variable:

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This Hurst exponent value is indicating that our data is a persistent one, but we have to keep in mind that our data set is too small to draw such a conclusion. For example, if you want to calculate Hurst exponent in Python using the ‘hurst’ library, it requires you to give at least 100 data points.

 

Disclaimer: All investments and trading in the stock market involve risk. Any decisions to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.

To download the code in this article, visit QuantInsti website and the educational offerings at their Executive Programme in Algorithmic Trading (EPAT™).

This article is from QuantInsti and is being posted with QuantInsti’s permission. The views expressed in this article are solely those of the author and/or QuantInsti and IB is not endorsing or recommending any investment or trading discussed in the article. This material is for information only and is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad-based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation by IB to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.


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Alpha Architect - SEC Cybersecurity Requirements and Advisors


Webinar Recording

In case you missed it! Watch it on IBKR’s YouTube Channel:
 

https://youtu.be/jzjlbxD0Mw8

 

Cybersecurity

 

In this webinar, Patrick Cleary will demystify the world of cybersecurity and provide actionable next steps for financial advisors looking to implement a best in class cybersecurity program. He will outline what resources are available, how to get started writing a cybersecurity manual, and what regulators will be looking for when an examination occurs. Most importantly, Patrick will highlight several low cost action items that many Advisors can do themselves to build a robust program from scratch.

 

Speaker: Patrick, R. Cleary, Alpha Architect

Sponsored by:   Alpha Architect

 

Information posted on IBKR Quant that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Quant are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results


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Machine Learning-Based Classification of Market Phases


By Dr. Dimitrios Geromichalos, RiskDataScience UG.
This article was first posted on
QuantNews.

 

Excerpt:

The experience of the recent years as well as research results and regulatory requirements suggest the consideration of market regimes. Nevertheless, the largest part of today’s financial risk management is still based on the assumption of constant market conditions. Currently, neither “stressed” market phases nor potential bubbles are determined in an objective way.

Machine learning procedures, however, enable a grouping according to risk aspects and a classification of the current market situation. RiskDataScience has already developed procedures to identify market phases. Market regimes can be determined on the basis of flexible criteria for historical time series. The current market conditions can be assigned to the respective phases. Thus, it is possible to determine if the current situation corresponds to past stress or bubble phases. In addition, historic stress scenarios can be detected in a systematic way.

Visit QuantNews to explore the Market Phases development process or continue reading below on Machine Learning Approaches.

 

Machine Learning Approaches

For the analysis of the relevant market data, several data science / machine learning algorithms can be considered and implemented with tools like Python, R, Weka or RapidMiner. Here, the following groups of algorithms can be discerned:

  • Unsupervised learning algorithms: These algorithms can be used for the determination of “natural” clusters and the grouping of market data according to predefined similarity criteria. This requires appropriate algorithms like kmeans or DBSCAN as well as economic and financial domain expertise. Also, outlier algorithms can be used to detect anomalous market situations, e.g. as basis for stress test scenarios.
     
  • Supervised learning algorithms: The algorithms (e.g. Naive Bayes) are “trained” with known data sets to classify market situations. Then, new data – and especially the current situation – can be assigned to the market phases. For a risk-oriented analysis, market data differences (e.g. in the case of interest rates) or returns (e.g. in the case of stock prices) must be calculated from the market data time series as a basis for the further analysis. Further, a “windowing” must be conducted, viz. the relevant values of the previous days must be considered as additional variables.

 

Use Case: Analysis of Illustrative Market Data

The analysis described below was based on a market data set consisting of the DAX 30 index, the EURIBOR 3M interest rate, and the EURUSD FX rate. The time period was end of 2000 till end of 2016. For the calculations, consistently daily closing prices were used as basis for the return (DAX 30, EURUSD) and difference calculations (EURIBOR 3M). Eventual structural breaches were adjusted and missing return values were replaced by zeros. The windowing extended to the last 20 days.

 

Chart

 

Image Source: QuantNews.
Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.

 

Visit QuantNews to explore the market data time series analysis, and to read the full article: https://www.quantnews.com/machine-learning-based-classification-market-phases/

 

 

About the Author:
Dr. Dimitrios Geromichalos, CEO / Founder RiskDataScience UG (haftungsbeschraenkt)
Web: riskdatascience.net
Email: riskdatascience at web.de
Twitter: @riskdatascience

About QuantNews:

Quantitative Trading, Algorithmic Trading, System Trading, Robot Trading and More: https://www.quantnews.com/

Quant

This article is from QuantNews and is being posted with QuantNews’ permission. The views expressed in this article are solely those of the author and/or QuantNews and IB is not endorsing or recommending any investment or trading discussed in the article. This material is for information only and is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad-based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation by IB to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.


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Automatic Full Compilation of Julia Programs and ML Models to Cloud TPUs


Author: Keno Fischer and Elliot Saba

Excerpt:

Google's Cloud TPUs are a promising new hardware architecture for machine learning workloads. They have powered many of Google's milestone machine learning achievements in recent years. Google has now made TPUs available for general use on their cloud platform and as of very recently has opened them up further to allow use by non-TensorFlow frontends. We describe a method and implementation for offloading suitable sections of Julia programs to TPUs via this new API and the Google XLA compiler. Our method is able to completely fuse the forward pass of a VGG19 model expressed as a Julia program into a single TPU executable to be offloaded to the device. Our method composes well with existing compiler-based automatic differentiation techniques on Julia code, and we are thus able to also automatically obtain the VGG19 backwards pass and similarly offload it to the TPU. Targeting TPUs using our compiler, we are able to evaluate the VGG19 forward pass on a batch of 100 images in 0.23s which compares favorably to the 52.4s required for the original model on the CPU. Our implementation is less than 1000 lines of Julia, with no TPU specific changes made to the core Julia compiler or any other Julia packages.

Read the full paper here: https://arxiv.org/pdf/1810.09868.pdf

 

 

About Julia Computing

Julia is the fastest modern open-source language for data science, machine learning and scientific computing. Julia provides the functionality, ease-of-use and intuitive syntax of R, Python, Matlab, SAS or Stata combined with the speed, capacity and performance of C, C++ or Java. Julia also provides parallel and distributed computing capabilities out of the box, and unlimited scalability with minimal effort.

About the authors: Keno Fischer is Julia Computing co-founder and CTO, and Eliot Saba is the Senior Research Engineer. Keno Fischer can be contacted at

Julia Computing provides products, training and consulting to make Julia easy to use, easy to deploy and easy to scale in your organization. Email us: info@juliacomputing.com

 

This article is from Julia Computing and is being posted with their permission. The views expressed in this article are solely those of the author and IB is not endorsing or recommending any investment or trading discussed in the article. This material is for information only and is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad-based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation by IB to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.


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