How can you use stock price forecasting models to make better investment decisions?

Stock price forecasting models are algorithms or statistical methods used to predict the future prices of stocks. These models can range from simple moving averages to complex machine learning algorithms, incorporating various factors such as historical price data, financial ratios, industry trends, and macroeconomic indicators.

Existem vários, recentemente utilizei o prophet, um modelo de machine learn de fácil compreen??o e que funciona muito bem para a??es, é importante ressaltar que é preciso entendimento da linguagem python para poder utilizá-lo.

Accuracy: Look for models with a proven track record of accuracy in predictions. Complexity vs. Usability: While more complex models may offer better accuracy, they also require more data and computational resources. Choose a model that you can understand and apply effectively. Data Availability: Ensure you have access to the necessary data required by the model. Market Conditions: Some models perform better in certain market conditions than others. Consider the current economic environment and market trends.

I would argue the assumption that a model that predicted price changes in the past will be good at predicting price changes in the future is just wrong. Of course we have to work with assumptions, but your basic assumption should be: the best predictor of a share price is its current price. No model is able to forecast share prices. If there would be such a model, the word would be a different place.

The stock market has so many variables at play that it is very hard to accurately forecast a stock price. There are many unknown factors that cannot be built into a model to accurately predict a stock price (for example, black swan events or short reports that send the stock price tanking down all of a sudden, etc.). A better approach would be to look at multiple data points (i.e. various forecasting models and what stock price those models predict) to triangulate a stock price that falls within a consensus range.

Aplica??o pode ser feita da seguinte maneira, se extrai os dados de uma fonte, transforma os mesmos em duas tabelas denominando X e Y, após isso poderá aplicar um modelo de machine learn que funciona perfeitamente por exemplo o prophet.

Data Preparation: Collect and clean the historical data required by the model. This might include stock prices, volume, financial statements, and market indicators. Model Training: Train the model using historical data to identify patterns and relationships that can predict future stock prices. Validation: Test the model on a separate data set to evaluate its predictive accuracy and make adjustments as needed. Implementation: Apply the model to current data to generate stock price forecasts. Use these forecasts as one of several tools in your investment decision-making process.

I started my first AI company 13 years ago and worked on my first R&D project for a large institution on stock predictions 10 years ago. The biggest issue with stock prediction models is that some train/test/perform well in Bull, Bear and neutral markets. (Many other layers but for 3 for simplicity) They struggle with the same things people do. When will the market shift? So they are largely useless unless you have an incredibly powerful (billions of dollars) inference engine ala RenTech to determine conditional weights or intelligent people building process on top of models. Most people violate the rules of Fooled by Randomness when they over-pitch their model efficacy.

Current stock market forecasting methods have several limitations. The volatile natural of stock values makes it difficult to predict accurately. Historical data and technical indicators, which are commonly used in these methods, may not capture all relevant factors. Additionally, the complexity of stock market data poses challenges in creating accurate prediction models. Disappearing gradient is a fundamental issue faced by current models, especially recurrent neural networks. Furthermore, the entry of new investors into stock market adds to the uncertainty of predictions.

The borders of predicting share prices are that there is no way to predict a future share price. Yes, we build models that we think are best situated to predict the future, but you just can not. The medallion fund somehow seems to have been able to generate above market returns consistently, but thats about it. When you look at then long term returns (10+ years), you simply do not beat the market.

Stock forecasting models encounter hurdles such as market complexity, data limitations, and the unpredictable nature of financial markets. Additionally, factors like overfitting, black swan events, and regulatory constraints pose significant challenges to accurate predictions and informed decision-making.

Stock price forecasting models cannot accurately account for market manipulation (which happens all the time). Because forecasting models cannot predict what price manipulation may (or may not) occur, this creates a very big limitation to the models.

Stock price forecasting models are meant to guide investment decisions, not serve as an investors brain. Models break all the time, trusting them with your own money or client capital is a risky game. Traditional point forecasts don't offer you the conviction you need to make strong decisions. Probabilistic forecasting (looking at the entire distribution) when done correctly, can offer you a much better view of the risks associated with a specific investment decision.

O modelo de previs?o n?o pode ser levado em considera??o somente, é preciso entendimento dos movimentos macro e micro presentes, fora a análise técnica como Price action, fibonacci, que s?o primordiais na tomada de decis?o, ou seja, quanto mais meios de mitigar os riscos melhor.

The scientific reasearch in regards to the question whether you can preditc share price movements is clear. It is a random walk in the short run and a regression to the mean in the long run. Buy the market, keep costs as low as possible, stay calm and dont sell in downturns.