  To predict the future movements of stock markets, numerous studies
concentrate on daily data and employ various machine learning (ML) models as
benchmarks that often vary and lack standardization across different research
works. This paper tries to solve the problem from a fresh standpoint by aiming
to predict the weekly movements, and introducing a novel benchmark of random
traders. This benchmark is independent of any ML model, thus making it more
objective and potentially serving as a commonly recognized standard. During
training process, apart from the basic features such as technical indicators,
scaling laws and directional changes are introduced as additional features,
furthermore, the training datasets are also adjusted by assigning varying
weights to different samples, the weighting approach allows the models to
emphasize specific samples. On back-testing, several trained models show good
performance, with the multi-layer perception (MLP) demonstrating stability and
robustness across extensive and comprehensive data that include upward,
downward and cyclic trends. The unique perspective of this work that focuses on
weekly movements, incorporates new features and creates an objective benchmark,
contributes to the existing literature on stock market prediction.
