Can AI predict the stock market?

December 23, 2023

Yes, artificial intelligence (AI) can predict the stock market, but never with 100% accuracy. As with all predictions, only time can tell if the predictions come true or not, and how accurate they were. The future is always uncertain because it has not been created yet. 

This article discusses what is a prediction, how are predictions made, observing trends to make predictions, forecasting the weather, and predicting the stock market. Reading will prime brains to understand the process of making predictions, improve prediction skills, and realize that the future is always uncertain and to some extent unpredictable. 

What is a prediction?

A prediction is a statement about what may happen in the future. Predicting is saying what will happen in the future. 

The word "prediction" comes from the French word prédiction and the Medieval Latin word predictionem. Predictionem comes from the Latin word praedictio, which means "a foretelling". Praedictio is a noun of action from the past-participle stem of praedicere, which means "assert, proclaim, declare publicly". The prefix "pre-" means "before". The "-dict" portion of the word comes from roots meaning to speak or say. To "pre-dict" means foretell. Some synonyms of "predict" are forecast, foretell, prognosticate, and prophesy.

A prediction is a prophecy, a declaration concerning future events. Predictions are often based on knowledge or experience, but not always. A prediction can be an educated guess based on careful study of facts and evidence, or it can be a total guess without any rational basis. Some common synonyms of predict are: foretell, forecast, prognosticate, prophesy. Other synonyms of prediction include: augury, divination, and projection. 

Predictions can be guesses, but usually derived observation and experience. For example, if humans observe the sun "rising" on the east every morning, humans can predict that the sun will rise on the east tomorrow morning. In psychology, a prediction is an attempt to foretell what will happen in a particular case, generally on the basis of past instances or accepted principles. 

How are predictions made?

Predictions are made using a variety of methods across different fields:

Expert Research: In some cases, experts in a field use their knowledge and experience to make educated guesses about future events or trends. Surveys, interviews, and other research methods help in predicting consumer behavior, market trends, and preferences.

Statistical Modeling: These models analyze historical data to identify patterns and relationships that can be used to predict future outcomes. Regression analysis, time series analysis, and machine learning algorithms fall under this category.

Simulation: Complex systems can be simulated to predict outcomes. This is often used in fields like economics, weather forecasting, and engineering. Oftentimes, rather than predicting a single future, multiple scenarios are created based on different assumptions to simulate and anticipate a range of possible outcomes. Different probabilities may be assigned to the different potential scenarios. The probabilities can be illustrated using a simple pie chart graphic.

Predictive Analytics: Combining data analysis, statistical techniques, and machine learning to forecast future trends, behaviors, or events. Time series analysis, trend analysis, and extrapolation are used to predict future values based on historical data trends.

All of these methods have their strengths and limitations, and often, a combination of approaches might be used to make more accurate predictions.

What are trends?

Trends refer to the general direction in which something tends to move or change over time. They represent patterns or shifts in behavior, attitudes, preferences, or phenomena that can be observed and analyzed within a specific context or industry. Trends can manifest in various areas:

• Social Trends: Changes in societal norms, values, or behaviors, like attitudes toward mental health, diversity and inclusion, or social activism.
• Health and Wellness Trends: Changes in lifestyle choices, fitness routines, diet preferences, or wellness practices that gain popularity over time.
• Consumer Trends: These involve shifts in consumer behavior, preferences, and purchasing habits. For instance, the move towards sustainable products or the increasing adoption of digital services.
• Fashion Trends: Changes in clothing styles, designs, colors, and materials that become popular for a certain period.
• Technological Trends: Advancements and developments in technology, such as the proliferation of computers, the internet, smartphones, and the rise of AI. 
• Market Trends: Patterns in the stock market, real estate, or other financial markets that indicate the direction in which prices, demand, or supply are moving.

Understanding and predicting trends can be essential for businesses, industries, policymakers, and individuals to anticipate changes and adapt accordingly to stay relevant or take advantage of emerging opportunities.

Trends are patterns or shifts in behavior, preferences, technologies, markets, or societal norms that show a consistent direction of change over time. They provide insight into the current state of affairs and offer clues about potential future developments. Trends are observed through data analysis, observations, and monitoring changes within specific domains.

Understanding and predicting trends can be essential for businesses, industries, policymakers, and individuals to anticipate changes and adapt accordingly to stay relevant or take advantage of emerging opportunities.

What is the relationship between trends and predictions?

By observing and identifying historical and current trends, forecasters can make better educated guesses and predictions about future trends. The relationship between trends and predictions is quite intertwined:

• Basis for Predictions: Trends serve as the foundation for making predictions. By analyzing trends, patterns, and historical data, one can extrapolate potential future developments or outcomes.
• Predictive Insights: Understanding trends allows for better predictive insights. If a particular trend has been consistent over time, it might suggest a likelihood of continuation in the future.
• Identifying Future Scenarios: Trends help in simulation and scenario planning. They allow analysts to create multiple future scenarios based on different trajectories of ongoing trends, aiding in better decision-making.
• Predictive Models: Trends are often incorporated into predictive models. Whether in statistical models, machine learning algorithms, or forecasting techniques, trends are a crucial input for predicting future outcomes.
• Anticipating Changes: Recognizing emerging trends enables individuals and organizations to anticipate changes, adapt strategies, innovate, or capitalize on opportunities that these trends might present.

In essence, trends provide the empirical basis upon which predictions can be made and explained. Predictions leverage the knowledge derived from trends to anticipate and forecast potential future developments, behaviors, market shifts, or technological advancements. However, it's important to note that while trends offer valuable insights, predictions based on them are inherently uncertain due to the complex and dynamic nature of various factors influencing future events. 

When making predictions trying to forecast the future, it is important to acknowledge that the future has not been created yet and is always uncertain by default. Any unknown variable can change everything, impacting even the most sound and well-researched predictions. For example, although extremely unlikely, it is always possible that the world may end today before tomorrow's sunrise. Humans can predict with high accuracy that the sun will "rise" (Earth will rotate overnight) tomorrow, but never with  100% certainty. 

There is always a higher than zero probability, even if miniscule, that a predicted future event will not materialize and that things will turn out in unpredictable ways. The uncertainty principle of quantum mechanics shows that the universe is not deterministic, but rather probabilistic. The future is being created at this very moment. Humans live in a matrix of creation, or "creatix". The present marks the point of demarcation between the past and the future. The present is the time filter where the uncertain future becomes a past certainty. 

Predicting the Weather

Weather forecasting has improved dramatically in recent years due to a large extent to advances in computing and artificial intelligence. Weather forecasting involves a combination of observations, data analysis, computer modeling, and meteorological expertise to predict future atmospheric conditions. Here's a general overview of the process:

• Data Collection: Meteorologists collect vast amounts of data from various sources. This includes information from weather stations, satellites, weather balloons, buoys, radar systems, and other instruments that measure parameters like temperature, humidity, wind speed, pressure, and more.
• Numerical Weather Prediction (NWP): This forms the backbone of modern weather forecasting. Numerical models use mathematical equations to simulate the behavior of the atmosphere. These models take the collected data as input and use complex algorithms to predict how the atmosphere will evolve over time.
• Model Initialization: Meteorologists use current observational data to initialize these models. The initial state of the atmosphere is crucial for accurate predictions. Data assimilation techniques merge observed data with model simulations to create the starting conditions for the forecast.
• Modeling and Prediction: The models simulate the atmosphere's behavior forward in time, breaking it into small grid cells and computing changes in weather variables at each grid point. These models run multiple simulations with slightly different initial conditions to account for uncertainties. AI algorithms are accelerating modeling and improving forecasting.
• Forecast Production: Meteorologists analyze model outputs, apply their expertise, and create forecasts for different timeframes (short-term, medium-term, long-term) and specific regions. They generate weather maps, predictions of temperature, precipitation, wind patterns, etc.

Recent advances in weather forecasting include:

• More Data: Enhanced satellite technology and more advanced radar systems provide more precise and frequent observations of weather patterns. International cooperation among meteorological agencies allows for the sharing of data, which contributes to more comprehensive global forecasting systems.
• Improved Modeling and Computer Simulation: Advances in computing power allow for higher-resolution models, providing more detailed forecasts for smaller geographical areas and shorter timeframes. Improved techniques for integrating observational data into models help in initializing forecasts more accurately. Running multiple model simulations with slight variations in initial conditions helps to assess forecast uncertainty and probability, providing more reliable predictions.
• Machine Learning and AI: These technologies are being integrated into forecasting models to improve accuracy and speed in interpreting large amounts of interconnected atmospheric data.

These advancements collectively contribute to more accurate and timely weather forecasts, benefiting various economic sectors like agriculture, transportation, disaster preparedness, and all other aspects of daily human life.

Predicting the Stock Market 

There are three, and only three, possibilities for stock market prices: go up; go down; or stay the same. Despite that limited "menu", predicting the stock market is complicated. For instance, it is more complicated than forecasting the weather because they are many more variables involved. The totality of variables involved in weather forecasting are just one segment of the variables affecting economic performance and stock market prices. The weather is just one of many variables involved in economic activity. Predicting economic activity also includes human psychological variables about consumer sentiment and investor confidence / risk tolerance that are harder to predict. 

Predicting whether stock prices are going to go up or down, and for how much, is fun and can prove very lucrative when mastered proficiently. In general, stock market prediction involves analyzing past market data, identifying patterns, and applying price forecasting methodologies. Here are some common approaches:

• Economic Indicators: Market analysts study macroeconomic indicators such as gross domestic product (GDP), inflation, interest rates, employment, and geopolitical events to anticipate market movements. Changes in these indicators often affect investor sentiment and market trends.
• Fundamental Analysis: Analysts assess a company's financial performance, earnings, assets, and management to determine valuation targets and ranges. This analysis considers economic indicators, industry trends, company earnings reports, and news to predict how these factors will impact stock prices.
• Technical Analysis: This method involves analyzing historical market data, primarily price and volume, using charts and indicators to identify patterns and trends. Traders use tools like moving averages, support/resistance levels, and chart patterns to predict future price movements based on historical behavior.
• Sentiment Analysis: Analyzing news, social media, and expert commentary, analysts can gain insights into market psychology in trying to predict price movements. AI natural language processing applications are being used to gauge public sentiment towards specific stocks or the market as a whole.
• Quantitative Models: Mathematical and statistical models using historical data and algorithms to predict future stock prices. These models can range from simple regression analysis to complex machine learning algorithms trained on large datasets.
• Machine Learning and AI: Advanced algorithms are used to process vast amounts of data, identify patterns, and make predictions. Neural networks, deep learning models, and reinforcement learning techniques are applied to predict stock prices based on historical patterns and real-time data. Algorithms are being trained on data to learn patterns and make predictions. Techniques like neural networks, decision trees, and support vector machines are used for classification, regression, and clustering tasks.

Predicting stock markets accurately is extremely challenging due to the complexities and uncertainties involved. Market dynamics can be influenced by unexpected events, human behavior, geopolitical factors, and unpredictable unknowns, making predictions inherently uncertain. Many investors and analysts use a combination of the predicting methods above and listen to multiple sources to make informed decisions. However, they acknowledge that no method can guarantee 100% accuracy or even consistent predictions over time. The future of the stock market is as uncertain as the future itself.

Creatix Stock Market Prediction

The U.S. stock market will set a new record high shortly after Christmas day of 2023. The U.S. economy is almost in "perfect" shape with low inflation (below 2% in the last 6 months), low unemployment (below 4%), strong GDP growth (5% in the third quarter), and the stock market almost at record high territory. This is amazing and not what most experts predicted. Many experts had forecasted a recession followed by a period of stagnation for 2023. The predictions were wrong. 

On Friday, December 22, 2023, the Standard and Poor (S&P) 500 index closed at 4,754.63. This is less than 1% away from the S&P 500's all-time high of 4,796.56, reached almost two years ago, on January 3, 2022. The market is .009% away from the record. Next week is Christmas and Chances are that a Santa Claus rally will bring a new record high. A mini bubble of positive and irrational optimism is forming, which will push stocks up to impressive record highs. 

From the record highs established in early winter of 2024, stocks will recede sharply by or before the spring of 2024. By mid summer in 2024, there will be upward movement without reaching new records.  In the fall of 2024, there will be great uncertainty tied to the U.S. presidential elections. An "unexpected" crisis of some sort whether in the U.S. or elsewhere in the world will most likely materialize and "shock" the market downward over 20% in a rapid correction. From bearish territory, the market will gradually recover and eventually turn bull again, setting new record highs by late 2026. The up and down cycle will continue in a slope upward trajectory. 

There are countless little things that will go wrong and cause market fluctuations. National debt is considerably high. Global warming is a threat to economic growth. There are also big geopolitical risks that may or may not materialize. A third world war (WWIII) could begin depending if or when China opts to reclaim Taiwan by force, and how the United States may react, in combination to current armed conflicts in Europe (Russia / Ukraine) and the Middle East. In the United States, continued polarization poses the risk of a constitutional crisis, a coup d'etat attempt, or even a potential second civil war (CW2). Globally, there are potential risks of another pandemic due to a COVID-like virus. Cosmologically speaking, there is always the potential for a meteorite hitting Earth and creating unimaginable chaos and havoc. Later on in the future, there is potential of an AI-powered coup d'globe. 

Whether any of the predictions above materialize or not, investing consistently (dollar cost averaging) on low cost and well diversified exchange traded funds (ETFs) with both U.S. and international exposure while keeping a good allocation between stocks, bonds, and cash should prove to be the best stock investment strategy in the long run. Real estate investing, especially in a primary residence with a reasonable mortgage, should also be part of the plan. Keeping a solid amount of savings in cash and certificates of deposits (CDs) is a must. Obtaining proper insurance including medical, casualty, and life is crucial. Learning to live significantly below available financial means is an essential skill and good wealth-building habit. Obtaining a good education and cultivating critical thinking for lifelong learning is super important. Staying healthy and keeping a Safety 1st approach are essential for a good life.

AI will help humans make better predictions and smarter decisions. AI will help humans make better investment and financial decisions. For the next three to five decades, AI will be a net positive for humans in all fields. Eventually, AI technology will grow to be so powerful that it will change dramatically the fabric of human society. From 50 to 100 years from now, humans will most likely begin to implement transhumanism medical therapies to physiologically augment human intelligence with AI. Humans will gradually replace their biology with AI technology. That is a sci fi prediction that may or may not materialize in the future. Humans alive in 2123 will judge if we were right or wrong. 

In the meantime, stay tuned. The best of AI is yet to come.

Creatix.one, AI for everyone.