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Algorithmic Trading

📊 Quantamental Trading Approach

Quantamental is a new trading approach that combines quantitative and fundamental analyses to forecast future market conditions. Trading and data mining are expected to converge in the near future.

In general, forecasting is the process of predicting future market conditions based on past and present data, as well as the analysis of key trends. All forecasting methods can be divided into two broad categories:

(i) Quantitative methods, based on mathematical models, and

(ii) Qualitative methods, based on educated guessing.

(1) Quantitative Forecasting

Quantitative forecasting involves predicting future data based on past data. It requires historical numerical data and assumes that recognizable patterns in the data series will continue into the future. This method is more effective for short- to mid-term decision-making.

There are two main types of quantitative methods:

(i) time-series methods, which use simple historical trends and patterns in the data series to generate forecasts;

(ii) explanatory methods, which use additional data as inputs into the forecasting model and attempt to combine two or more variables.

Key quantitative forecasting approaches in finance include:

(1) Average Forecasting Approach: Assumes that future market data will equal the statistical mean of past data.

(2) Naive Forecasting Approach: Assumes the time series follows certain seasonality. This approach is useful for financial time series with complex patterns that are hard to recognize and predict. Naive forecasting is often used as a benchmark for comparing other models.

(3) Drift Forecasting Approach: A more advanced version of the Naive Approach, incorporating an increase or decrease over time, called a drift.

(2) Fundamental Analysis

Fundamental analysis is a method of valuing a financial instrument by examining and evaluating all relevant internal and external factors. These factors include financial, economic, social, political, strategic, and other quantitative and qualitative variables. The goal of fundamental analysis is to determine a 'fair value' that can be compared with the current market price.

Fundamental analysis uses a wide range of real data—such as macroeconomic indicators, industry analysis, balance sheets, earnings reports, and key data releases—to assess the value of a financial instrument.

While fundamental and quantitative analyses differ in core ways, they also share many similarities. For example, when evaluating shares, both methods consider market capitalization, sector classification, price/earnings ratio, and dividend policy. As a result, quantitative models can be used to optimize the outcomes of fundamental analysis.

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