BiMPC
The first multilingual, revolutionary system that combines portfolio optimization, portfolio risk analysis, investment style analysis, strategy performance comparison, and portfolio P&L monitoring in one platform.
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The first multilingual, revolutionary system that combines portfolio optimization, portfolio risk analysis, investment style analysis, strategy performance comparison, and portfolio P&L monitoring in one platform.
Learn More
MPC builds its mean-variance optimization engine on the Efficient Frontier concept proposed by Nobel laureate Harry Markowitz. MPC takes a theory long established in academia and the financial industry and raises it to a new level — every parameter required for mean-variance optimization can be computed by the system or entered manually, and the engine identifies the portfolio with the highest expected return or yield and the lowest volatility or risk, giving you full control over fine-tuning the optimization.
Unlike comparable systems on the market that only allocate among risky products (e.g., equities, mutual funds), MPC can simultaneously combine risky and risk-free products (e.g., deposits) in portfolio optimization, building portfolios that more closely match real-world operations.
Asset Allocation Optimization
Identifies the lowest-risk, highest-expected-return portfolio using Markowitz and Black-Litterman algorithms.
The most powerful Mean-Variance Optimization engine
MPC builds its mean-variance optimization engine on the Efficient Frontier concept proposed by Nobel laureate Harry Markowitz. MPC takes this theory — long established in academia and the financial industry — and raises it to a new level. Every parameter required for mean-variance optimization can be computed by the system or entered manually, and the engine identifies the portfolio with the highest expected return or yield and the lowest volatility or risk, giving you full control over fine-tuning the optimization.Black-Litterman Allocation
After Markowitz's Efficient Frontier, the market saw the emergence of another allocation approach — Black-Litterman. It is an innovative model combining Bayes' theorem, the Capital Asset Pricing Model (CAPM), and Markowitz's Efficient Frontier.
Efficient Investment Method
Efficient Investment Method — Core-Satellite Asset Allocation
Core-Satellite Allocation has long been used by professional institutional investors at home and abroad. It splits the portfolio into a core allocation and a satellite allocation. The core allocation is built from stable-return core funds, aiming to match or slightly exceed the benchmark index; the satellite allocation is built from higher-volatility funds and aims to beat the market to boost overall portfolio performance. Each month, the parent fund contributes a fixed amount to the sub-funds; when a sub-fund hits its take-profit point, the gain is rolled back to the parent fund, locking in the core's profits. This cycle repeats — in effect, the efficient investment method is the best way to combine lump-sum and regular-fixed-amount investing.
Through configurable parameters (any debit date, segmented stop-loss/take-profit conditions, scaling up or down, redemption settings, unlimited number of sub-funds, etc.), MPC's core-satellite module lets you build a wide range of core-satellite investment models at will, and immediately backtest them against historical data to evaluate profitability and effectiveness. By replacing manual core-satellite return calculations with systematic computation, the system delivers accurate, fast results and dramatically improves efficiency.
Models can also be benchmarked against indices or other investment approaches (lump-sum, regular-fixed-amount), making it easy to see how a core-satellite model performs relative to indices or alternative strategies.
Portfolio P&L Monitoring
Daily P&L flags monitor portfolio profitability at a glance.
Even an optimal portfolio needs regular P&L monitoring to keep it in top shape. MPC calculates the latest P&L for each fund in the portfolio and for the portfolio itself, based on the initial investment amount and current value.
Every fund and portfolio can have configurable upper and lower P&L limits, each accompanied by a P&L flag. The flag is updated daily from the latest fund NAVs, and different flag colors indicate different P&L states.
Fund Style Analysis
Using Sharpe's and Brinson's distinctive methods to break down funds and return contributions inside a portfolio.
Return-Based Style Analysis
A fund manager's unique investment philosophy and style have a decisive impact on fund performance. Only a scientific algorithmic analysis can fully reveal the manager's investment philosophy and style.Brinson Analysis
A strong-performing portfolio reflects distinctive insight or methodology in the investor's or operator's asset allocation and/or stock selection skills. MPC applies Brinson analysis to break down the sources of performance for fund or equity portfolios. It cleanly isolates the asset allocation effect, the stock selection effect, and the combined effect (Policy) within a portfolio, so you can see how each individual capability contributes to overall portfolio performance — a critical quantitative reference for portfolio performance analysis.Risk/Return Rolling Window Analysis
Risk and return for funds or equity portfolios can shift with the manager, the market, or simply time. MPC uses a Style Rolling Window Map to show how risk/return have evolved historically for a fund or portfolio, making it easy to see how products or portfolios have performed through market swings or manager changes — a critical reference for investment and decision-making.
Risk Analysis - VaR
Accurately assess maximum portfolio risk using variance-covariance, Monte Carlo, and historical simulation methods.
Risk Analysis - Value at Risk
Forecasts of expected returns and volatility for products or portfolios are directly tied to the accuracy of asset allocation and the effectiveness of risk management. MPC 2011 applies the latest financial engineering techniques, using the algorithmic models most highly regarded by both academia and the industry, to estimate expected returns and volatility and compute VaR — significantly improving the precision of asset allocation and risk control.VaR Models
Variance-Covariance Method
Historical Simulation
Monte Carlo Simulation
Volatility Estimation Methods
Simple Moving Average
Assumes historical returns are normally distributed — the simplest way to compute volatility.Exponentially Weighted Moving Average (EWMA)
Weights returns differently based on recency, giving more recent data heavier weight, so short-term volatility shifts are captured.GO-GARCH(p,q)
Builds on Carol Alexander's O-GARCH, using van der Weide's GO-GARCH to estimate portfolio volatility.The three volatility estimation methods produce different covariance matrices, risk measures, and VaR values, all of which can be backtested and forward-tested across algorithms.
Portfolio Backtesting
Compare historical return trajectories for portfolios.
Unlike traditional approaches — where wealth managers or advisors recommend funds based on fund fact sheets or self-prepared investment proposals — the fund backtesting module lets users dynamically tune investment parameters against historical data through a user-friendly interface, with simple but powerful settings. The result: accurate, fast simulations across different investment modes (lump-sum, regular-fixed-amount, irregular/variable-amount, etc.), making it easy to compare P&L across approaches and find the most profitable way to invest in a given fund or fund portfolio.
The fund/portfolio backtesting module supports the following backtest types:
Fund lump-sum / portfolio lump-sum
Fund regular-fixed-amount / portfolio regular-fixed-amount
Configurable monthly investment date and amount.Fund irregular/variable-amount / portfolio irregular/variable-amount
Configurable stop-loss, take-profit, scale-up-on-loss, and similar conditions.Index backtesting
Run any of the above strategies with an index as the underlying.For investment P&L, the system presents the following detail fields by strategy:
Total amount invested
Maturity value
Current value
Gain amount
Cumulative return
Number of take-profit and scale-up-on-loss events
Shortest and longest profitable holding periods, and more
Portfolio Reports
Instantly generate clean, well-designed reports in Word, PDF, HTML, or Excel.
MPC can produce a transition report from the current portfolio to the target portfolio, including the following analyses on the target portfolio:
Asset allocation analysis
Portfolio Monte Carlo simulation
Portfolio backtesting
Portfolio rolling-window performance evaluation
Current vs. target portfolio
Reports can be exported in Word, PDF, HTML, or Excel formats.
Product and research teams in banks, brokerages, and insurance; fund managers; private wealth advisors; CFAs; portfolio managers at investment advisory firms and trust companies; and more.