In our research, we stick to the rules of writing:
In our research, we calculate the following indicators:
Further, we will provide a complete description of the indicators.
So, a hypothesis is a problem statement for research. By formulating a hypothesis, we decide on what exactly we want to test, whether it is the interconnection between financial instruments or the impact of an event on the market
We prefer testing hypotheses that are related to statements of economic theory, market "gurus" or generally accepted principles.
We don't apply combinatorics, don't select events and don't use neural networks and various black-boxes. We analyze how each economic event affects the market.
There are many factors that influence changes in share prices, exchange rates and other instruments. A large sample of data helps us to achieve high level of accuracy in research. We are trying to include various segments of financial markets and historical data for several decades in research to test hypotheses.
We tend to conduct research with a sample of more than 300 cases. A research can be based on more than 100,000 cases.
As market segments, we analyze U.S. stocks, Russian stocks, indices, currency pairs, energy commodities, metals and other goods. There are more than 80 instruments in 5 segments.
We use the most widespread instruments of the segment, for instance, we consider only blue chips, DOW30 stocks among all U.S. stocks. They are instruments with a high trading volume, and consequently they are less influenced by random events, financial interventions, etc
When conducting research on economic events, for instance, on the release of unemployment data, we examine the data during the time intervals from one day to three weeks. We have not selected any methods for intraday trading. And there is no point in examining one-month interval or a longer time interval, as economic data is updated every month.
While examining technical analysis signals, we take H1 and D1 timeframes. The dynamics of rates within timeframes less than H1 are influenced by an excessive number of random factors, while for W1 timeframes, there is a small sample of events.
In this section we present the results obtained from research and review them.
When examining technical analysis signals, the signal should have an effect on most segments, otherwise, we come to the conclusion that there's no influence.
We use the phrase "the influence has been identified" to demonstrate it. We assume that an economic event is a signal indicating changes in the market, rather than infer cause-and-effect relationships.
Correlation is a statistical relationship between variables. The correlation is determined by calculating the Pearson linear correlation coefficient and the correlation coefficient.
The correlation coefficient is calculated according to the following equation:
We calculate the correlation coefficient by comparing the following values:
Correlation is calculated both with and without a time shift. For example, we can compare the change in oil prices today with the change in the EURUSD rate tomorrow or the day after tomorrow by calculating the correlation between oil prices and the EURUSD rate.
The correlation with time shift makes it possible to predict the dynamics of rates and make a profit.
To evaluate the obtained values of the correlation coefficient in research, we use the Chaddock scale.
|less than 0.1||no correlation|
|from 0.1 to 0.3||low correlation|
|from 0.3 to 0.5||moderate correlation|
|from 0.5 to 0.7||remarkable correlation|
|from 0.7 to 0.9||strong correlation|
|from 0.9 to 0.99||very strong correlation|
|from 0.99 to 1||functional correlation|
Various events, such as formation of a head and shoulders pattern in technical analysis or seasonal effects, may affect rates of financial instruments. A linear correlation is not suitable for assessing correlation between such events and changes in rates, that's why we introduced our own indicator – momentum.
Momentum allows us to assess the market reaction and its movements.
Momentum is the rate of the average change in the price of a financial instrument over a period of time after a certain market event.
Momentum is calculated according to the formula:
P – the relative change in the rate of a financial instrument over a certain period of time, expressed as percentage.
n — the number of events;
A positive value of momentum indicates the profitability of the event, while a negative value indicates its unprofitability. It is not enough to find out if the value of momentum is positive or negative, as it also should be statistically significant.
A significant momentum is a value greater than the required minimum value.
Minimum values of momentum set by the MarketCheese team:
If there is a significant momentum and a sufficient sample size, we can assert that the influence of the event on the market has been identified.
Share of profitable positions, or SPP, is another indicator set by the MarketCheese team that assesses the impact of events.
SPP refers to the share of profitable trading positions out of the total number of positions and is calculated according to the formula:
SPP is a kind of a qualitative indicator used to evaluate effectiveness. The higher SPP is, the more often you will make profitable trades.
Evaluating volatility of financial instruments is important as instruments with higher volatility are considered to be more risky compared to indicators of less volatile instruments. At the same time, high volatility makes it possible to generate extra income.
We apply an arithmetic method when calculating the volatility:
Where max – the maximum value of the instrument over a period of time, min – the minimum value, n – the number of periods.
We evaluate volatility in our research to identify the influence of an event on the market. If the volatility does not change, the event may not affect significantly the change in rates of financial instruments.
In our research, we rely on the following values when evaluating volatility:
|less than 2%||low|
|from 2 to 5%||medium|