This article “HMM-Based Classification of Stock Market Stages” was written by Sergey Okun – Senior Financial Analyst at I Know First, Ph.D. in Economics.

Highlights

• Identifying the stage of the market enables traders to adjust their investment strategies to take advantage of the current market conditions.
• The Hidden Markov Model allows us to determine the stock market stages.
• We uncover patterns in the S&P500 index and VIX from January 1^st, 2000 to April 6^th, 2023 by using HMM to identify periods of bull and bear markets.

Why Should We Care About the Stock Market Stage?

Being able to identify the stage of the stock market is crucial for traders as it enables them to make well-informed decisions on when to buy or sell stocks. The stock market experiences various stages, including a bull market, marked by rising prices and positive sentiment, and a bear market, characterized by falling prices and negative sentiment. The classification or categorization of these stages varies among experts, and here are some commonly utilized frameworks:

1. Economic cycle stages: Some analysts categorize the stock market stages based on the broader economic cycle. These stages can include expansion, peak, contraction, and trough.
2. Price trend stages: Others may categorize the stages based on price trends in the market. These stages can include accumulation (when prices are rising), mark-up (when prices are rapidly increasing), distribution (when prices are leveling off), and mark-down (when prices are falling).
3. Sentiment stages: Another way to categorize the stages of the stock market is by sentiment or investor behavior. These stages can include optimism (when investors are confident and buying), euphoria (when investors are overly optimistic and buying aggressively), anxiety (when investors are uncertain and selling), and panic (when investors are fearful and selling rapidly).

By identifying the stage of the market, traders can adjust their investment strategy to take advantage of the current market conditions. For example, in a bull market, traders may want to buy stocks and hold on to them for a longer period of time, while in a bear market, they may want to sell stocks or consider short selling. In addition to the overall market stage, traders may also analyze individual stocks or sectors to determine their current stage. This can help them identify stocks that are likely to perform well in the current market environment. Overall, understanding the stage of the stock market and individual stocks can help traders make more informed investment decisions and potentially increase their returns while minimizing risk.

Identify a Stage by Hidden Markov Model

Machine learning models can be used to uncover patterns in the stock market by analyzing large amounts of data, identifying trends and relationships, and making predictions based on historical patterns. Hidden Markov Models (HMMs) can be useful in analyzing stock market data by allowing us to model the underlying structure of the data and make predictions based on that structure. HMM is a type of unsupervised learning algorithm. In unsupervised learning, the algorithm learns from unlabeled data and tries to identify hidden patterns or structures in the data.

HMMs are a type of statistical model that are often used in time series analysis. They allow us to model a sequence of observations, such as stock prices over time, as a series of states that are not directly observable. The HMM assumes that each observation is generated by a hidden state, and the states are connected by a set of transition probabilities that determine the probability of moving from one state to another. To use an HMM to analyze stock market data, we would first need to define the states of the model. These states could represent different market conditions, such as bullish or bearish trends, or they could represent different patterns in the data, such as oscillations around a mean value. Once we have defined the states, we can use historical data to estimate the transition probabilities between the states and the probability distribution of observations given each state.

Once the model is trained, we can use it to make predictions about future market conditions or stock prices. For example, we can use the model to predict the probability of a particular market condition occurring based on current or historical data. We can also use the model to generate a sequence of predicted stock prices based on the estimated probabilities of transitioning between states. Overall, HMMs can be a powerful tool for analyzing stock market data, allowing us to model the underlying structure of the data and make predictions based on that structure. However, it is important to note that stock market data is highly complex and volatile, and no model can provide perfect predictions.

Uncover the S&P500 Stages

Let us implement the unsupervised machine learning algorithm for determining the stock market stages by using HMM. We use the “GaussianHMM” package in Python for our task. We use daily data for the S&P500 index and VIX from January 1^st, 2000 to April 6^th, 2023. For the training process, we use close price data for VIX and we calculate returns for the S&P500 index based on adjusted close prices. Also, we assume four market stages.

Figure 1 provides the means for four stages. We can characterize them as follows: 1. the growth with moderate volatility; 2. the significant drop with high volatility (crash); 3. the decline with high volatility; 4. the significant growth with low volatility.

In Figure 2, we can notice periods of price clustering that enables us to identify periods of “bull” and “bear” markets. For instance, 2022 is a period of the “bear” market, while 2012 – 2016 is a period of the “bull” market. Also, we can say that from March 27, 2022, to April 6, 2023, we stay in the yellow stage (the growth with moderate volatility).

I Know First Algorithm – Seeking the Key &  Generating Stock Market Forecast

The I Know First predictive algorithm is a successful attempt to discover the rules of the market that enable us to make accurate stock market forecasts. Taking advantage of artificial intelligence and machine learning and using insights of chaos theory and self-similarity (the fractals), the algorithmic system is able to predict the behavior of over 13,500 markets. The key principle of the algorithm lays in the fact that a stock’s price is a function of many factors interacting non-linearly. Therefore, it is advantageous to use elements of artificial neural networks and genetic algorithms. How does it work? At first, an analysis of inputs is performed, ranking them according to their significance in predicting the target stock price. Then multiple models are created and tested utilizing 15 years of historical data. Only the best-performing models are kept while the rest are rejected. Models are refined every day, as new data becomes available. As the algorithm is purely empirical and self-learning, there is no human bias in the models and the market forecast system adapts to the new reality every day while still following general historical rules.

I Know First has used algorithmic outputs to provide an investment strategy for institutional investors. Below you can see the investment result of our S&P 500 Stocks package which was recommended to our clients for the period from January 1^st, 2020 to March 29^th, 2023 (you can access our forecast packages here).

The investment strategy that was recommended by I Know First accumulated a return of 74.92%, which exceeded the S&P 500 return by 52%.

Conclusion

Being able to identify the stage of the stock market is crucial for investors as it enables them to make well-informed decisions on when to buy or sell stocks. Machine learning models, such as Hidden Markov Models (HMMs), can be useful in analyzing stock market data by allowing us to model the underlying structure of the data and make predictions based on that structure. By implementing the unsupervised machine learning algorithm for determining the stock market stages by using HMM, we can uncover patterns in the S&P500 index and VIX from January 1^st, 2000 to April 6^th, 2023. This allows us to identify periods of “bull” and “bear” markets, and adjust our investment strategy to take advantage of the current market conditions.

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Please note-for trading decisions use the most recent forecast.