What Can AI Do for Investment Portfolios? A Case Research


Artificial intelligence (AI)-based methods are being more and more utilized in investing and portfolio administration. Their contexts, utility, and outcomes fluctuate broadly, as do their moral implications. Yet for a expertise that many anticipate will remodel funding administration, AI stays a black field for much too many funding professionals.

To deliver some readability to the topic, we zeroed in on one explicit AI fairness buying and selling mannequin and explored what it might deliver by way of advantages and risk-related prices. Using proprietary information supplied by Traders’ A.I., an AI buying and selling mannequin run by our colleague Ashok Margam and crew, we analyzed its choices and all-around efficiency from 2019 to 2022.

Traders’ A.I. has few constraints available on the market positions it takes: It can go each lengthy and quick and flip positions at any level within the day. By every day’s closing bell, nevertheless, it fully exits the market, so its positions usually are not held in a single day. 

So how did the technique fare over completely different time intervals, buying and selling patterns, and volatility environments? And what can this inform us about how AI could be utilized extra broadly in funding administration?

Traders’ A.I. outperformed its benchmark, the S&P 500, over the three-year evaluation interval. While the technique was impartial with respect to lengthy vs. quick, its beta over the time-frame was statistically zero.

Traders AI Model vs. S&P 500 Monthly Equity Curve ($10k Investment)

Chart Showing Traders AI Model vs. S&P 500 Monthly Equity Curve ($10k Investment)

Traders’ A.I. leveraged moments of upper skewness to realize these outcomes. While the S&P 500 had unfavorable skewness, or a robust left tail, the AI mannequin displayed the other: proper skewness, or a robust proper tail, which implies Traders’ A.I. had few days the place it generated very excessive returns.

AI Model S&P 500
Mean 0.00111881 Mean 0.00064048
Standard Dev. 0.005669 Standard Dev. 0.01450605
Kurtosis 11.1665 Kurtosis 13.1015929
Skewness 1.59167732   Skewness -0.62582387

So, the place was the mannequin most profitable? Was it higher going lengthy or quick? On excessive or low volatility days? Does it select the appropriate days to sit down out the market?

On the latter query, Traders’ A.I. really prevented buying and selling on excessive return days. It might anticipate excessive threat premium occasions and choose to not take a place on which course the market will go.

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Traders’ A.I. carried out higher on a market-adjusted foundation when it went quick. It made 0.13% on common on its quick days whereas the market misplaced 0.52%. So the mannequin has performed higher predicting down days than it has up days. This sample is mirrored in bear markets as nicely, the place Traders’ A.I. generated extra efficiency relative to bull markets.

AI Model’s Average Return S&P 500’s Average Return
When Model Is Active 0.1517% -0.0201%
When Model Sits Out 0% 0.8584%
When Model Is Long 0.1786% 0.6615%
When Model Is Short 0.1334% -0.5215%
When Model Is Long and
Short in a Day
0.1517% -0.0201%
On High-Volatility Days 0.1313% -0.0577%
On Low-Volatility Days 0.0916% 0.1915%
In Bull Markets (Annual) 17.0924% 46.6875%
In Bear Markets (Annual) 20.5598% -23.0757%
In Bull Markets 0.0678% 0.1853%
In Bear Markets 0.0816% -0.0916%

Finally, the AI mannequin carried out higher on high-volatility days, beating the S&P 500 by 0.19% a day on common whereas underperforming on low-volatility days.

AI Model’s Return Percentage vs. VIX Percentage Change

Chart showing AI Model's Return Percentage vs. VIX Percentage Change

All in all, Traders’ A.I.’s outcomes exhibit how one explicit AI fairness buying and selling mannequin can work. Of course, it hardly serves as a proxy for AI purposes in investing typically. Nevertheless, that it was higher at predicting down days than up days, succeeded when volatility was excessive, and prevented buying and selling all collectively earlier than large market-moving occasions are crucial information factors. Indeed, they trace at AI’s huge potential to remodel funding administration.

For extra on this subject, don’t miss “Ethics and Artificial Intelligence in Investment Management: A Framework for Professionals,” by Rhodri Preece, CFA.

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All posts are the opinion of the writer. As such, they shouldn’t be construed as funding recommendation, nor do the opinions expressed essentially mirror the views of CFA Institute or the writer’s employer.

Image credit score: ©Getty Images / Svetlozar Hristov

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Derek Horstmeyer

Derek Horstmeyer is a professor at George Mason University School of Business, specializing in exchange-traded fund (ETF) and mutual fund efficiency. He at present serves as Director of the brand new Financial Planning and Wealth Management main at George Mason and based the primary student-managed funding fund at GMU.

Nicholas Guidos

Nicholas Guidos is a senior at George Mason University pursuing his bachelor of science diploma in enterprise with concentrations in finance and monetary planning and wealth administration. He is serious about monetary markets, choices, futures, wealth administration, and monetary evaluation. He is the George Mason University Financial Planning Association chapter president and plans to acquire his CFP certification and CFA constitution after commencement.

Lance Nguyen

Lance Nguyen is a senior at George Mason University pursuing a bachelor of science diploma in electrical engineering. He is serious about synthetic intelligence, excessive frequency buying and selling, technical evaluation, monetary evaluation, and derivatives markets. Currently, he’s engaged on the deployment of TradersAI in addition to acquiring a Series 3. After commencement, he might be working as a controls engineer whereas pursuing a grasp’s diploma in monetary engineering.


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