The Challenge of Investing

Summary:

  • For decades, value investment has been seen as the top investment strategy, embraced by Warren Buffett.
  • However, those relying on value investing have had both their ups and downs, suggesting the strategy is not perfect.
  • Furthermore, dating all the way back to 50-s, it does not account for the new technologies and artificial intelligence.
  • Today, hedge funds and private investors have been able to persistently generate tremendous returns by utilizing AI in their trading.
  • Algo-trading is a great way to enhance informed human decision-making and optimize the investment strategy.

Value Investing

Benjamin Graham’s The Intelligent Investor is a book heralded by many, including Warren Buffett, as the best book on investing that has ever been written. The book covers the history of the stock market in the United States and discusses why so many people fail to make money. Buffett, who has achieved a 20.9% annualized gain from 1964 to 2017, practices the theory of “value investing” that was taught to him by Graham. Value investing, as defined by Graham, is “An investment operation in one which, upon thorough analysis, promises safety of principal and an adequate return.” This style of investing has withstood the test of time, as the S&P 500 – widely seen as the benchmark to measure investment returns against – returned only an average of 9.9% per year over the same time period that Buffet returned 20.9%.

New York Stock Exchange

Beating the Market

Moreover, there are only a select few investors that have also beat the S&P 500 for an extended period of time. There are several hedge funds and money managers that have years where they achieve tremendous returns – sometimes more than 100% – but these same groups can never sustain their incredible results. Value investing time and time again has been the only well-known strategy to out-perform the market. The strategy however, is not as simple as plugging in financial data into a formula; it requires a significant amount of discretion and discipline in order to emulate Graham’s idea.

The Intelligent Investor was written in 1949 (there have been more recent updated versions), and therefore it does not fully take into account new technologies and how they might affect the underlying theory of value investing. Since then, there have been several hedge funds that have drastically outperformed both the S&P 500 and Warren Buffet. More importantly, these hedge funds have been reliant on algorithmic trading strategies, a concept that was not fully addressed as a viable investing strategy by Graham in his book. Graham had held that the only way to consistently outperform the market was through his method, but this seems to no longer be the case.

Warren Buffett

New Tech in Finance

Renaissance Technologies, one of the world’s leading hedge funds that uses algorithmic and quantitative trading strategies, has a fund called the Medallion Fund which has achieved a 71.8% annual return from 1994 to 2014. While the content of their trading strategies is highly secretive, their techniques are known to be mathematical and statistical in nature, as they often hire scientists, mathematicians, and physicists.

In the age of technology, the theories laid out by Graham, and practiced by Buffett among others, are being put to the test. Ever since Graham’s book was written, we have been able to point to countless examples of hedge fund managers making great returns, then eventually blowing up in failure from one terrible year. While one disastrous year at Renaissance Technologies hypothetically could once again prove Buffett and Graham right, all indications point to their continued success. Renaissance Technologies is not an anomaly either. Other algorithmic-based hedge funds such as Two Sigma and D. E. Shaw have consistently outperformed the market and value investors such as Buffett.

There is clearly still a place for value investing in the financial market, as Buffett and other value-based investors continue to show success. However, there has emerged a new type of investment strategy that uses computers, statistics, and complex algorithms in place of the thorough human analysis like that done by value investors.

Algorithmic trading is not without its shortcomings however, as there are times when a computer or formula can be clearly wrong in ways that a human could easily see. On the flip side, an algorithm is also capable of forming connections between financial data that show opportunities that a human would not be capable of producing. I Know First supports the use of algorithms, but also the use of human discretion in making trading decisions.

I Know First’s algorithm achieved a 54% return for the period of August 2015-August 2018 compared to the S&P 500’s 42% return for the same period. Though the accuracy of the forecasts is not 100%, investors have been able to derive significant returns from selected stocks using signal and predictability in an appropriate strategy. An investor utilizing I Know First’s service will be able to gain insights of the market that can help them outperform major indexes.

I Know First Algorithm vs. S&P 500

I Know First’s algorithm uses a machine learning algorithm that uses thousands of indices including stocks, commodities, and currencies from around the world to identify opportunities for investors to either buy or sell. As the algorithm obtains more financial data, it is able to find relationships between and among indices, which in turn helps it make better predictions.

About the I Know First Algorithm

The I Know First self-learning algorithm analyses, models, and predicts the stock market. The algorithm is based on Artificial Intelligence (AI) and Machine Learning (ML) and incorporates elements of Artificial Neural Networks and Genetic Algorithms.

The system outputs the predicted trend as a number, positive or negative, along with a wave chart that predicts how the waves will overlap the trend. This helps the trader to decide which direction to trade, at what point to enter the trade, and when to exit. Since the model is 100% empirical, the results are based only on factual data, thereby avoiding any biases or emotions that may accompany human derived assumptions. The human factor is only involved in building the mathematical framework and providing the initial set of inputs and outputs to the system. The algorithm produces a forecast with a signal and a predictability indicator. The signal is the number in the middle of the box. The predictability is the number at the bottom of the box. At the top, a specific asset is identified. This format is consistent across all predictions.

Our algorithm provides two independent indicators for each asset – Signal and Predictability.

The Signal is the predicted strength and direction of movement of the asset. Measured from -inf to +inf.

The predictability indicates our confidence in that result. It is a Pearson correlation coefficient between past algorithmic performance and actual market movement. Measured from -1 to 1.

Here is the detailed description of the heatmap.

Our Stock Picking Method

The method in this evaluation is as follows:

We take the top X most predictable assets, and from them we pick the top Y highest signals.

By doing so we focus on the most predictable assets on the one hand, while capturing the ones with the highest signal on the other.

For example, a top 100 predictability filter with a top 50 signal filter means that on each day we take only the 100 most predictable assets, and then we pick from them the top 50 assets with the highest signals.

These outputs can then be utilized by a trader as a decision support system and assist them in the trading process especially given that modern trades are increasingly computerized making it even more difficult for a human to be making to correct decisions. Tools like the I Know First algorithm allow humans to continue to trade and keep up with the fast-paced computerized trading without compromise in the more detailed and personal aspect behind investment trading.

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