64 research outputs found
Predicting risk/reward ratio in financial markets for asset management using machine learning
Financial market forecasting remains a formidable challenge despite the surge
in computational capabilities and machine learning advancements. While numerous
studies have underscored the precision of computer-generated market
predictions, many of these forecasts fail to yield profitable trading outcomes.
This discrepancy often arises from the unpredictable nature of profit and loss
ratios in the event of successful and unsuccessful predictions. In this study,
we introduce a novel algorithm specifically designed for forecasting the profit
and loss outcomes of trading activities. This is further augmented by an
innovative approach for integrating these forecasts with previous predictions
of market trends. This approach is designed for algorithmic trading, enabling
traders to assess the profitability of each trade and calibrate the optimal
trade size. Our findings indicate that this method significantly improves the
performance of traditional trading strategies as well as algorithmic trading
systems, offering a promising avenue for enhancing trading decisions
SCGG: A Deep Structure-Conditioned Graph Generative Model
Deep learning-based graph generation approaches have remarkable capacities
for graph data modeling, allowing them to solve a wide range of real-world
problems. Making these methods able to consider different conditions during the
generation procedure even increases their effectiveness by empowering them to
generate new graph samples that meet the desired criteria. This paper presents
a conditional deep graph generation method called SCGG that considers a
particular type of structural conditions. Specifically, our proposed SCGG model
takes an initial subgraph and autoregressively generates new nodes and their
corresponding edges on top of the given conditioning substructure. The
architecture of SCGG consists of a graph representation learning network and an
autoregressive generative model, which is trained end-to-end. Using this model,
we can address graph completion, a rampant and inherently difficult problem of
recovering missing nodes and their associated edges of partially observed
graphs. Experimental results on both synthetic and real-world datasets
demonstrate the superiority of our method compared with state-of-the-art
baselines
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