IBKR Quant Blog


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Quant

Two Centuries of Global Factor Premiums


Authors: Baltussen, Swinkels, van Vliet
Title: Global Factor Premiums
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3325720

 

Abstract:

We examine 24 global factor premiums across the main asset classes via replication and new-sample evidence spanning more than 200 years of data. Replication yields ambiguous evidence within a unified testing framework with methods that account for p-hacking. The new-sample evidence reveals that the large majority of global factors are strongly present under conservative p-hacking perspectives, with limited out-of-sample decay of the premiums. Further, utilizing our deep sample, we find global factor premiums to be not driven by market, downside, or macroeconomic risks. These results reveal strong global factor premiums that present a challenge to asset pricing theories.

Notable quotations from the academic research paper:

"In this paper we study global factors premiums over a long and wide sample spanning the recent 217 years across equity index (but not single securities), bond, currency, and commodity markets.

The first objective of this study is to robustly and rigorously examine these global factor premiums from the perspective of ‘p-hacking’.

We take as our starting point the main global return factors published in the Journal of Finance and the Journal of Financial Economics during the period 2012-2018: time-series momentum (henceforth ‘trend’), cross-sectional momentum (henceforth ‘momentum’), value, carry, return seasonality and betting-against-beta (henceforth ‘BAB’). We examine these global factors in four major asset classes: equity indices, government bonds, commodities and currencies, hence resulting in a total of 24 global return factors.

We work from the idea that these published factor premiums could be influenced by p-hacking and that an extended sample period is useful for falsification or verification tests.

 

 

To learn more about this paper, view the full article on Quantpedia website:
https://quantpedia.com/Blog/Details/two-centuries-of-global-factor-premiums

 

 

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

There is a substantial risk of loss in foreign exchange trading. The settlement date of foreign exchange trades can vary due to time zone differences and bank holidays. When trading across foreign exchange markets, this may necessitate borrowing funds to settle foreign exchange trades. The interest rate on borrowed funds must be considered when computing the cost of trades across multiple markets.

Futures are not suitable for all investors. The amount you may lose may be greater than your initial investment. Before trading futures, please read the CFTC Risk Disclosure. A copy and additional information are available at ibkr.com.

This material is from Quantpedia and is being posted with Quantpedia’s permission. The views expressed in this material are solely those of the author and/or Quantpedia and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material 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 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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Byte Academy - Introduction To Python For Data Analysis


In case you missed it! Watch the webinar recording on the IBKR YouTube Channel:

https://youtu.be/YxIwgo_lYig

 

Python-Byte-Academy

 

This "Learning Bytes" series webinar, held in conjunction with Python, FinTech and Data Science coding school Byte Academy, will provide an introduction to Python for data analysis. Due to its analytical capabilities, Python is highly popular in the finance and data science industries. We'll start with an overview of Python and its packages for data analysis, then walk through examples using Excel files to demonstrate basic data manipulation.

Sponsored by: Byte Academy

 

The analysis in this material is provided 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 IBKR to buy, sell or hold such investments. 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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API Case Study in Pair Trades - IBKR Traders' Academy Python API Course


Python

 

This lesson offers a practical way to wrap your knowledge of Python and IBKR API by exploring a case study with advanced order types. Be sure to consult the Study Notes to learn about Pair-trading, a popular strategy in algorithmic trading, where an instrument is bought and a related instrument is sold short.

Finish the course by testing your knowledge with the Final Exam!

https://gdcdyn.interactivebrokers.com/en/index.php?f=25228&course=22

 

Trading on margin is only for sophisticated investors with high risk tolerance. You may lose more than your initial investment.

The order types available through Interactive Brokers LLC’s Trader Workstation are designed to help you limit your loss and/or lock in a profit. Market conditions and other factors may affect execution.  In general, orders guarantee a fill or guarantee a price, but not both.  In extreme market conditions, an order may either be executed at a different price than anticipated or may not be filled in the marketplace.

The analysis in this material is provided 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 IBKR to buy, sell or hold such investments. 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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Five Indicators To Build A Trend Following Strategy - Part III


See part II in this series to learn more about Bollinger Bands.

MACD (Moving Average Convergence Divergence)

The Moving Average Convergence Divergence indicator (MACD) is a comparative analysis of two moving averages for two different datasets. Depending on the bandwidth of the time series, you can assess the price fluctuations for two different stretches of time. Say one for a span of a month and another for 200 days. Comparison of the moving average for these two data sets is done based on three main observations viz. convergence, divergence and dramatic rise.

 
How to use MACD in trend following strategies:
If the price fluctuations for one data set is less than the moving average, while for the other data the fluctuations are above the moving average, it is wiser to take a short position on the stock because the price variation is not stable. 

Plotting MACD in python for trend following strategies:
The Python code is given below: 

# MACD

data['macd'], data['macdsignal'], data['macdhist'] = ta.MACD(data.close, fastperiod=12, slowperiod=26, signalperiod=9)

data[['macd','macdsignal']].plot(figsize=(10,5))

plt.show()


The graph plotted is shown below:

 

Quant

 

In the next article, Rekhit will discuss RSI (Relative Strength Index).

 

*Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.

Learn more QuantInsti here https://www.quantinsti.com

To learn more about Python and R, visit QuantInsti website and their educational offerings at their Executive Programme in Algorithmic Trading (EPAT™).

Trading on margin is only for sophisticated investors with high risk tolerance. You may lose more than your initial investment.


This material is from QuantInsti and is being posted with QuantInsti’s permission. The views expressed in this material are solely those of the author and/or QuantInsti and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material 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 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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Towards Better Keras Modeling - Part I


 

Quant

 

The field of deep learning is frequently described as a mix of art and science. One of the most "art-sy" parts of the field, in my experience, is the subject of network topology design - i.e., choosing the right geometry, size, depth, and type of network.

Machine learning practitioners develop rules of thumb for reasonable starting points - and learn heuristics for how to iterate towards optimality.

In this post, I'll walk you through my early exploration with Talos, a simple framework that automates the workflow of conducting hyperparameter optimization on Keras models. I'm relatively new to Talos, so this brief tutorial should be no substitute for the project's documentation or support forum.

 

As a sample dataset, I'll make use of data from a recent contest at Numerai, an interesting project that is best thought of as Kaggle, Quantopian, and Ethereum all rolled into one.

Numerai provides datasets to any data scientists interested in developing prediction models - much like Kaggle. Those who feel they have trained useful models can choose to enter a weekly contest for a chance to win cash prizes.

To access this sample dataset, I'll make use of Numerox, an API interface to Numerai's data contests.

This post will cover:

  • Installing Talos and Numerox
  • Downloading and preparing data from Numerai
  • Setting up and executing a coarse parameter sweep on a Keras model
  • Analyzing the results
  • Conducting a second, "finer" scan on the parameter space
  • Reaching a final model

    If you'd like to replicate and experiment with the below code, you can download the source notebook for this post by right-clicking on the below button and choosing "save link as"

 

Python

 

In the next post, the author will show us how to set up Talos and Numerox.

 

----------------

About The Alpha Scientist

I'm Chad, aka The Alpha Scientist. I've created The Alpha Scientist blog to explore the intersection of my two professional passions: locating "alpha" in market inefficiencies and applying data science methods. If you've found this post useful, please follow @data2alpha on Twitter and forward to a friend or colleague who may also find this topic interesting. https://alphascientist.com/

 

This material is from The Alpha Scientist and is being posted with The Alpha Scientist’s permission. The views expressed in this material are solely those of the author and/or The Alpha Scientist and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material 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 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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Disclosures

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The material (including articles and commentary) provided on IBKR Quant Blog is offered for informational purposes only. The posted material is NOT a recommendation by Interactive Brokers (IB) that you or your clients should contract for the services of or invest with any of the independent advisors or hedge funds or others who may post on IBKR Quant Blog or invest with any advisors or hedge funds. The advisors, hedge funds and other analysts who may post on IBKR Quant Blog are independent of IB and IB does not make any representations or warranties concerning the past or future performance of these advisors, hedge funds and others or the accuracy of the information they provide. Interactive Brokers does not conduct a "suitability review" to make sure the trading of any advisor or hedge fund or other party is suitable for you.

Securities or other financial instruments mentioned in the material posted are not suitable for all investors. The material posted does not take into account your particular investment objectives, financial situations or needs and is not intended as a recommendation to you of any particular securities, financial instruments or strategies. Before making any investment or trade, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice. Past performance is no guarantee of future results.

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