IBKR Quant Blog


1 2 3 4 5 2 55


Quant

Harvesting Risk Premia: Investing in things that go up - Combining Risk Factors


Authors: Kris Longmore

Excerpt:

 

Combining Risk Factors
There are two broad approaches to constructing portfolios of risk factors:

Strategic Allocation

This involves constructing a portfolio that aims to deliver a certain level of performance regardless of the prevailing conditions.

Such portfolios will typically have proportionately significant dollar exposure to long and intermediate term government bonds, smaller dollar exposure to equities, and a minor allocation to gold. But many variations on this theme exist.

The significant exposure to low volatility, positive carrying, fixed income assets could give these sorts of portfolios a relatively smooth performance curve, at the expense of the additional upside that’s possible from exposure to equities.

Tactical Allocation

This involves moving into and out of various risk exposures based on some signal or forecast. The well-known Dual Momentum strategy is a simple, yet extreme, example of this approach, as it shifts the entire allocation between US equities, international equities and government bonds. Most variants of tactical allocation instead involve re-weighting the portfolio’s allocation to be overweight certain assets at certain times, while still maintaining some allocation to other factors.

Many variations on the tactical allocation theme exist, and a significant proportion of the funds management industry is based on this approach.

But here’s the thing about tactical allocation: it’s hard. The premise of this approach is that a skilled manager can outperform a permanent allocation using clever timing and selection of factors. But using this approach, it’s all too easy to mis-time active decisions and wind up with something that underperforms a more strategic, low-turnover portfolio. So you can end up spending a lot of time and effort for little, or even negative reward.

 

Read the full article on Robot Wealth Blog:
https://robotwealth.com/blog/

 

 

Learn more about Robot Wealth here: https://robotwealth.com/

This material is from Robot Wealth and is being posted with Robot Wealth’s permission. The views expressed in this material are solely those of the author and/or Robot Wealth 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.


23310




Quant

Top 10 Machine Learning Algorithms For Beginners - Part 4


See the previous post of this series to learn more about Decision Trees.

 

Random Forest

random forest algorithm was designed to address some of the limitations of decision trees.

Random Forest is comprised of decision trees, which are graphs of decisions representing their course of action or statistical probability. These multiple trees are mapped to a single tree, which is called a Classification and Regression (CART) Model.

To classify an object based on its attributes, each tree gives a classification, which is said to “vote” for that class. The forest then chooses the classification with the greatest number of votes. For regression, it considers the average of the outputs of different trees.

Quant

 

Random Forest works in the following way:

  1. Assume the number of cases as N. A sample of these N cases is taken as the training set.
  2. Consider M to be the number of input variables, a number m is selected such that m < M. The best split between m and M is used to split the node. The value of m is held constant as the trees are grown.
  3. Each tree is grown as large as possible.
  4. By aggregating the expectations of n trees (i.e., majority votes for classification, average for regression), anticipate the new data.

 

 

Stay tuned for the next installment in this series to learn about Artificial Neural Network!

 

 

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™).

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.


23255




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.


23173




Quant

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.


23214




Quant

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.


23115




1 2 3 4 5 2 55

Disclosures

We appreciate your feedback. If you have any questions or comments about IBKR Quant Blog please contact ibkrquant@ibkr.com.

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.

Any information provided by third parties has been obtained from sources believed to be reliable and accurate; however, IB does not warrant its accuracy and assumes no responsibility for any errors or omissions.

Any information posted by employees of IB or an affiliated company is based upon information that is believed to be reliable. However, neither IB nor its affiliates warrant its completeness, accuracy or adequacy. IB does not make any representations or warranties concerning the past or future performance of any financial instrument. By posting material on IB Quant Blog, IB is not representing that any particular financial instrument or trading strategy is appropriate for you.