Danylo

Danylo Malieiev
Economics & Finance Undergraduate | Seeking Internship in M&A / Investment Banking / Corporate
Finance
✉ [open contact info](look above in the "contact info" section) ✆ [open contact info](look above in the "contact info" section) ⚲ Bologna, Italy LinkedIn

PROFILE
Analytically rigorous Economics & Finance undergraduate at the University of Bologna with hands-on
experience building quantitative research frameworks and AI-driven models in Python. Passionate about M&A
and investment banking — eager to contribute to live deal processes, market mapping, and research in a
high-performance advisory environment. Combines strong financial fundamentals (DCF, Capital Budgeting,
regression analysis) with practical proficiency in AI tools and data-driven analysis.

EDUCATION
BSc in Economics and Finance 09/2023 – Present
University of Bologna Bologna, Italy
• Relevant Coursework: Statistics, Econometrics, Corporate Finance, International Finance, Financial
Economics
• Academic Focus: DCF valuation, Markowitz portfolio theory, Capital Budgeting, Regression Analysis

BSc in Artificial Intelligence 09/2022 – Present
Kharkiv National University of Radio Electronics Remote
• Relevant Coursework: Machine Learning, Data Structures & Algorithms, Advanced Linear Algebra,
Probability Theory
• Academic Standing: 82.5/100 — Top 15% of cohort (ranked 27/200)

PROJECTS & RESEARCH
Statistical Arbitrage & ML Research System 10/2025 – Present
Present Thesis Research
• Built a modular Python backtesting framework (~40 modules) implementing three strategy branches —
pairs trading (Engle-Granger cointegration, Kalman filter), factor model (Avellaneda-Lee PCA residuals),
and ML/DL signals — tested on S&P 500 daily equities and Binance USDM 15-minute perpetual futures
• Trained and evaluated gradient boosting (LightGBM) and 1D-CNN (PyTorch) models on financial time
series; meta-CNN achieved stable OOS ROC-AUC of 0.688 across three seeds, improving trade
selection winrate from 45% to 57% under honest walk-forward validation with embargo gap
• Identified and formally documented the realizability–stationarity dilemma in pairs trading: fixed hedge
ratio produces realizable but non-stationary spreads; dynamic Kalman estimation produces stationary
but non-realizable spreads — confirmed experimentally with a 93–95% winrate artifact vs. 44% honest
baseline
• Designed a rigorous validation toolkit (gross-vs-net P&L comparison, 2×2 signal inversion diagnostics,
IS/OOS split with anti-overfit optimizer, look-ahead prevention) — delivered a reproducible null result
across two independent asset classes (0/81 configurations profitable gross on crypto)
• Applied Monte Carlo–style parameter sweeps across 81 configurations; evaluated strategy robustness
using Sharpe ratio, CAGR, Max Drawdown, and per-trade Sharpe under realistic maker/taker execution
costs

SKILLS

Programming & AI Tools: Python (ML models, stochastic modeling, data pipelines), R (regression,
statistical modeling); proficient with AI productivity tools (ChatGPT, Claude, AI copilots) for research
acceleration and workflow automation
Financial Tools: Microsoft Excel (financial modeling, valuation), PowerPoint (pitchbooks, presentations);
familiarity with financial statement analysis
Languages: English (Proficient), Italian (B1), Russian/Ukrainian (Native)