The closing price of a company's stock tomorrow can be lower, higher, or the same as today's closing price. We have the following information on the closing price of a stock collected over one month: 20% of the days, the closing price was higher than the previous day's closing price 50% was lower than the previous day's and 35% was the same as the previous day's Based on this information, we know that the probability that tomorrow's closing price will be higher than today's is 20%. This is an example of using which of the following probability approach?

Question

BlackTom AI · Accepted Answer

The problem presents observed frequencies from a month of data and asks which probability approach this represents.
Option 1: Relative frequency probability. This approach uses the proportion of times an event occurred in the long run (empirical data) to estimate its probability. Here, the information given is empirical: on 20% of days the price went up, 50% it went down, and 35% it stayed the same. Interpreting tomorrow’s move as having probability based on these observed frequencies fits relative frequency reasoning, since the probability is derived from actual counts.
Option 2: A classical probability. This would require that all outcomes are equally likely and known in advance (e.g., a fair die with six sides). The scenario uses past observed frequencies, not assumed equal likelihood, so this does not fit classical probability.
Option 3: Subjective probability. This is based on personal belief or judgment rather than data. The problem relies on data from a month, not personal intuition, so this option is mismatched.
Option 4: Conditional probability. This requires the probability of an event given that another event has occurred (P(A|B)). The question presents unconditional frequencies from historical data, not probabilities conditioned on another event, so conditional probability is not the appropriate framing here.

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