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Algorithmic approach:
• Traditional stochastic models: These models rely on explicit mathematical algorithms that are often based on simplifying assumptions about data distribution. They use methods such as linear models, time series, ARIMA (AutoRegressive Integrated Moving Average) models, etc.
• AI (machine learning and deep learning): These approaches rely on learning from data rather than on the explicit specification of a mathematical model. Models are trained to recognize patterns and relationships in data using machine learning algorithms, and in the case of deep learning, through artificial neural networks with multiple hidden layers.
Ability to capture complex models:
• Traditional stochastic models: They are often less able to capture complex, non-linear patterns in the data because they rely on simpler assumptions and less flexible model structures.
• AI (machine learning and deep learning): These approaches are better suited for capturing complex, non-linear patterns in data, thanks to their ability to learn hierarchical data representations at various levels of abstraction.
Interpretability:
• Traditional stochastic models: They are often easier to interpret because they are based on explicit mathematical formulas.
• AI (machine learning and deep learning): These models are often more difficult to interpret due to their complex nature and opacity. However, there are techniques to explain the predictions of these models, but they can be more complex to implement.
Data requirements:
• Traditional stochastic models: They can often work with relatively small data sets and can provide reasonable results even with missing or noisy data.
• AI (machine learning and deep learning): These approaches generally require larger data sets for training, and they are often sensitive to data quality and quantity. They may require more data preprocessing to get meaningful results.
The LSTM (Long Short-Term Memory) RNN (Recursive Neural Network) model is indeed one of the popular models for forecasting time series, including stock market data. However, there is no single model that is always the “best” for stock market forecasting, as the effectiveness of a model depends on many factors, including the quality and quantity of available data, the forecast period, and the changing nature of financial markets.
Data commonly used for stock market forecasting generally includes stock prices (high, low, open, close), trading volume, and relevant macroeconomic data. Additionally, using corporate data such as financial reports, product and service data, business-specific events, etc., can also be useful in improving prediction performance.
Here are some important considerations when using an LSTM RNN model for multivariate stock market forecasting:
• Choice of features: It is important to select wisely which features to include in the model. This may include stock prices (high, low, open, close), trading volume, and other relevant data such as business data (e.g. revenue, earnings, profits, major announcements).
• Data preprocessing: Before training the LSTM model, it is essential to normalize the data, deal with missing values, and possibly apply smoothing or noise reduction techniques.
• Model architecture: The architecture of the LSTM model may vary depending on data complexity and specific forecasting needs. This may include the number of LSTM layers, the number of neurons in each layer, as well as the use of techniques such as attention to improve model performance.
• Model training and validation: Training the LSTM RNN model involves dividing the data into training and validation sets, and tuning the hyperparameters of the model to optimize forecasting performance. Using techniques such as cross-validation can help assess the generalizability of the model.
Choosing the historical period to train a prediction model depends on several factors, including:
• Stable market conditions: Financial markets can be subject to periods of volatility or stability. It is often a good idea to choose a historical period that represents a variety of market conditions, including periods of rise, fall, and stability, so that the model is able to generalize effectively to different situations.
• Data availability: Available historical data may have limitations in terms of availability and quality. It is important to choose a historical period for which you have good quality and complete data, so that the model can be properly trained.
• Temporal relevance: If you plan to predict in the short term, it may be useful to use more recent historical data as it is more likely to reflect current market conditions. On the other hand, if your prediction horizon is longer term, you can use a wider historical period to capture longer-term trends.
• The prediction objective: The historical period chosen may also depend on the specific objective of your prediction. For example, if you are focused on predicting short-term volatility, you can choose a historical period that includes periods of high volatility. If you are predicting long-term trends, a longer historical period may be appropriate.
In summary, the choice of the historical period depends on a variety of factors, including the stability of market conditions, data availability, temporal relevance, and the objective of prediction. It is important to choose a historical period that represents a variety of market conditions and that is adapted to the specific prediction horizon.
When comparing passive investment via ETFs (Exchange-Traded Funds) to active stock selection, especially with respect to long-term performance, several factors need to be taken into account, including risk management, costs, and the effectiveness of fund managers.
Passive investment (ETF):
• ETFs generally follow market indices, which means that their performance is linked to that of the underlying index. They offer instant diversification since they invest in a basket of stocks that are representative of the index.
• ETF management fees are generally lower than actively managed funds, which can increase net returns for long-term investors.
• Passive investors generally benefit from the stability of long-term investments and often avoid the volatility associated with active stock selection.
Active selection of titles:
• Active stock selection involves fund managers choosing individual stocks in an effort to outperform the market. This can lead to higher-than-market returns if managers make smart choices.
• However, research shows that most fund managers fail to consistently beat the market over the long term after accounting for management fees.
• Some funds like hedge funds use more sophisticated strategies, such as arbitration, derivatives trading, or the use of leverage to try to generate higher returns. However, these strategies may be riskier and less accessible for ordinary investors.
In general, ETFs often offer a simple, low-cost, and diversified investment option for long-term investors, while active stock selection can involve higher risks and greater costs. While some fund managers may outperform the market, most investors opt for passive strategies because of their efficiency and simplicity. However, each investor should assess their own goals, risk tolerance, and preferences before choosing an investment approach.
Rises in financial markets can be caused by a variety of economic, financial, and geopolitical factors. Here are some of the main causes of increases in the financial markets and the main indicators to watch out for:
In terms of the main indicators to watch out for, here are some of the main ones:
In summary, increases in financial markets can be influenced by a wide range of economic, financial, and geopolitical factors. Investors are closely monitoring key economic indicators, central bank monetary policies, and liquidity flows in the markets to assess the prospects for growth and returns.
Escaping the algorithms of asset management firms such as BlackRock and Citadel can be difficult, as these firms use sophisticated strategies and advanced algorithms to make investment decisions. However, here are some approaches that some investors are taking to try to stand out:
Demographics, the proliferation of fake news, and hidden interests in an information war context can influence financial market rules and create regulatory inconsistencies. Here's how these aspects can impact financial markets:
In response to these challenges, financial regulators may seek to put in place stronger rules for managing the money supply, monitoring electronic transactions, and regulating complex financial products. They may also seek to promote the transparency and accountability of market participants to reduce systemic risks and protect investors.
However, due to the complex and globalized nature of financial markets, it can be difficult to develop rules that manage to effectively regulate all aspects of financial activities. This can lead to regulatory inconsistencies and gaps, requiring constant supervision and adaptation by financial regulators.
The concept of drift and mean reversion are two important concepts in finance, but they are not necessarily the only ones or the most important ones. However, they reflect fundamental aspects of financial markets that may be influenced by universal principles observed in nature. Here is an overview of these concepts and their relevance:
These concepts can be considered to reflect universal laws observed in nature, especially with respect to the regularity of trends and cycles. For example, in nature, many phenomena follow regular cycles or average trends, such as seasons, plant growth cycles, or even the movements of planets.
However, it is important to note that financial markets are also influenced by numerous other factors, such as investor psychology, economic and political events, technological innovations, etc. Therefore, while drifting and averaging are important concepts, they do not capture the total complexity of financial markets.
Ultimately, in order to understand and succeed in financial markets, it is crucial to take into account a multitude of factors and concepts, in addition to drifting and averaging, and to adapt your strategies according to market conditions and investment goals.
Volume Profiles and Footprints are two market analysis tools that are widely used by traders to assess price behavior and market sentiment based on trading volume. Here is an overview of each of these concepts:
By combining Volume Profiles and Footprints, traders can gain a better understanding of market behavior and identify potential trading opportunities. For example, a strong accumulation of volume at a particular price level on the Volume Profile, combined with “Footprints” showing strong buying activity at that level, could indicate a strong level of support where traders could consider entering a long position. Likewise, a strong accumulation of volume at a price level with “Footprints” showing strong selling activity could indicate potential resistance where traders could consider taking short positions.
In summary, Volume Profiles and Footprints are powerful market analysis tools that help traders visualize and interpret market behavior based on trading volume. By using them effectively, traders can make more informed decisions and improve their trading performance.
Yes of course! The concept of supply and demand remains the most important in the markets.
The evolution of trading on Wall Street from the beginning of the 20th century to the present day has been marked by significant changes in traders' skills, strategies, and educational requirements. Here is an overview of the evolution of these two approaches:
Trader with only a school certificate (first half of the 20th century):
Trader with a doctorate (recent evolution):
The move towards more quantitative and mathematical strategies has been driven by several factors, including the increasing availability of market data, improved computing capabilities, and the goal of reducing transaction costs while maximizing returns. However, this does not mean that the price action approach has completely disappeared. Many traders continue to use technical analysis techniques and discretionary strategies based on observing price movements.
In summary, while traders in the first half of the 20th century focused primarily on experience and intuition, the recent evolution of trading on Wall Street saw the emergence of highly skilled traders with advanced mathematical skills and computer expertise, who exploit sophisticated quantitative models to make trading decisions.
To watch the interview on YouTube: https://www.youtube.com/live/hs32MFkVPZ8?si=w4VSP1O9KZtV-WqD
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