If you’re a voracious reader, you’ve probably run across articles discussing stock market forecasts. Yet, we will discuss why and how stock market predictions fail in this post. And I assure you that it will change your perspective and dispel your need for predictions.
1.Bias in selection
Many initiatives begin by arbitrarily choosing a stock to which an algorithm is to be applied; this stock is frequently a tech stock, like Apple or Amazon. The main justification for this is that these stocks are well-known and embedded in users’ daily lives. This is problematic because choosing stocks is a critical step in making financial decisions, which in and of itself calls for a model. For instance, if we compare Apple stock’s performance in 2019 to that of the larger SP 500 index, we can see that it fared almost 60% better. The profile for Amazon, the industry with the best performance in 2019, is largely the same. Nevertheless, in 2022, we can see that Apple’s stock had a difficult year, falling 27% over the course of the year, whereas Amazon’s shares have dropped 49% in 2022 and are on track to have their worst year since the dot-com crisis of 2000.
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2. Portfolio Development
A sound investing strategy must include risk control since it produces returns. Portfolio construction comes first and foremost after stock selection, which is the initial phase in the investment process. Many projections advocate purchasing or selling a stock, but they frequently do so under the assumption that the stock will make up the entirety of the projected portfolio. One of the best ways to reduce risk is to diversify a portfolio that has been designed with judgement. A successful ML investment approach takes into account both stock selection and portfolio creation.
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3. Improper Pre-processing Application
Stock price prediction cannot be done using any planned, consistent method. With the explicit presumption that training and test samples come from the same distribution, pre-processing has been used to take a transformation utilizing parameters from the training set that is relevant to the test set in the usual train/test split paradigm of machine learning. We can easily see how the stock price distribution has changed year over year, which indicates that the mean and standard deviations (SD) will also be erratic. There is no theoretical upper constraint on prices, except the application of other widely used techniques like min-max normalization. The price differencing transformation (stock price returns) that practitioners frequently use does not completely eliminate some of the unfavorable aspects of stock prices for buying and selling.
4. The Look-Ahead Bias
A considerable history of stock and macroeconomic fundamental data that we should be aware of can be retrieved with just a few lines of code. In many cases, beliefs pertaining to specific dates might not have actually been possible at that time. We can use stock fundamental information as an example. This information is dependent on details that are cited at an effective date that typically corresponds with the organization’s financial schedule; however, this disclosure isn’t made until months after the compelling date, delaying the best opportunity for planning. Due to changes to information from earlier periods that are often made a quarter after the underlying information was supplied, this becomes judgmental in the macroeconomic dataset. This is a particular challenge for short-term trading techniques.
5. The Project Isn’t Completed!
Many stock prediction projects will come to a conclusion similarly to a regular ML project with the disclosure of a performance metric like accuracy or RMSE with a line plot to test as compared to training performance. The logic behind this is that if the two lines are sufficiently close and the error is reasonable, the success rate of the project will be lower. These hasty judgements frequently leave out a crucial element in developing a winning plan. The repercussions of making a mistake are extremely real, thus investing cannot be reduced to a straightforward exercise to reduce an intangible error rate. The next stage should be to backtest this approach and determine profit/loss or returns as if it were used over an extended period of time.
Conclusion
Never before has it been simpler to implement both new and old prediction algorithms thanks to the revival of AI and ML. While much of machine learning can be applied to statistical programming, domain expertise is frequently neglected in favor of quick satisfaction.
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