The king of self-balancing scooters has moved into shared e-battery bikes.

When high-end electric vehicles adopted in-wheel motors, they reached a technological height that internal-combustion vehicles could only look up to. In fact, thanks to precise control capabilities, autonomous driving technology for electric vehicles is significantly less costly than for fuel-powered vehicles. From a technical roadmap perspective, EV technology delivers a 'dimensional downgrade'—a decisive, overwhelming advantage—over traditional fuel vehicles. Can you imagine a fuel-powered self-balancing scooter? There is only one company crowned the king of the electric self-balancing scooter market: Segway Ninebot (hereinafter 'Ninebot'). I. A Founding Story Rooted in Beihang University The world’s first self-balancing scooter was produced by Segway Inc. In 2002, Segway’s scooters began selling on Amazon and quickly gained global popularity—especially during the 2008 Beijing Olympics, when over 100 units were deployed for event services, capturing widespread attention. However, those early scooters were prohibitively expensive—priced at around ¥80,000 (approx. USD 11,000). In 2012, a group of Beihang University alumni identified this promising niche and founded Ninebot. In 2014, it joined Xiaomi’s ecosystem and secured investments from Xiaomi, Sequoia Capital China, and Shunwei Capital. At that point, Ninebot slashed the price of self-balancing scooters from ¥80,000 down to just ¥1,999. Overnight, these devices became accessible to ordinary consumers. For Segway, the sky fell. In 2015, Ninebot acquired Segway. Segway’s fate had been turbulent: in 2009, it was acquired by British entrepreneur Jimi Heselden—only for him to die tragically in 2010 while riding a Segway, losing control as he swerved to avoid a dog and plunging off a cliff. Such safety concerns ultimately capped the market potential of self-balancing scooters: across most countries, they struggle to obtain road-legal licenses. Their use remains largely confined to limited scenarios—such as residential community recreation or site patrols—making it easy for companies to hit an industry growth ceiling. To break through and achieve diversified transformation, Ninebot expanded into electric bicycles. It even revived the 'Segway' brand for its premium e-bike line. Yet, in its '2024 First-Half National Special Supervisory Inspection Report on Electric Bicycle Product Quality', China’s State Administration for Market Regulation listed 21 non-compliant manufacturers—including Ninebot. Specifically, its Orion TDR005Z ('Orion 5Z') electric bicycle was flagged for failing quality standards. II. A Stellar Semi-Annual Report Data Source: Tonghuashun iFind In the first half of 2024, the company’s revenue reached ¥6.666 billion—a 52.20% increase year-on-year. Net profit attributable to shareholders of the listed company amounted to ¥596 million, surging 167.82% compared to the same period last year. According to the semi-annual report, four key drivers underpinned this growth: First, product portfolio expansion: The company actively broadened its offerings, launching multiple new products—including electric self-balancing scooters, electric kick scooters, electric two-wheelers, all-terrain vehicles, service robots, and e-bikes—to meet diverse scenario-specific and user needs. Second, sustained R&D investment: The company consistently increased R&D spending to drive technological innovation, enhance product intelligence, and improve user experience. Third, strengthened brand value: It intensified brand-building efforts, ramped up marketing and advertising expenditures, and elevated brand visibility and influence. Fourth, global market expansion: The company pursued comprehensive globalization—covering both online and offline channels—and actively entered overseas markets.

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Factor Model Backtesting: A portfolio constructed using GRU model predictions delivered a 10-year cumulative return of 257.51%, outperforming the benchmark index (MSCI World, 247.25%), especially during periods of financial stability. Strategy Differences: An annual rebalancing strategy based on operating income yielded the strongest performance, whereas a quarterly rebalancing strategy based on total revenues proved more susceptible to seasonal fluctuations. Potential to Replace Human Forecasting: Automated forecasting surpasses human analysts in accuracy (measured by MSE) and enables scalable, probabilistic forecasts—addressing cost and coverage limitations inherent in manual analysis, particularly valuable for diversified global investing. Risk Assessment and Regulatory Compliance: Probabilistic forecasts (e.g., evaluated via the nCRPS metric) quantify uncertainty, helping enterprises and regulators assess financial stability and respond to black-swan events (e.g., the COVID-19 pandemic’s severe impact on the tourism industry). (C) Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data – CHEN – 2022 – Journal of Accounting Research – Wiley Online Library This paper, published in the top-tier academic journal *Journal of Accounting Research*, represents a landmark study in accounting research. It pioneers the integration of machine learning (ML) methods with granular financial data to improve prediction accuracy for the direction (increase/decrease) of firms’ earnings changes over the next year—and rigorously validates its superiority over traditional models and analyst forecasts. Key elements of the model include: • Sample: Annual financial data from U.S. public companies spanning 1994–2018, covering approximately 50,000 firm-year observations. • Features: • Traditional financial metrics: e.g., revenue, net income, debt-to-equity ratio, cash flow—standard accounting variables. • Granular financial data: High-dimensional data extracted from XBRL (eXtensible Business Reporting Language) filings, including over 800 detailed line items (e.g., R&D expenses, depreciation methods, customer concentration). • Industry- and size-adjusted variables: To control for industry effects and firm-size bias. • Algorithm Models: • Random Forest (RF): Captures nonlinear relationships and variable interactions. • Gradient Boosting Machine (GBM): Handles high-dimensional data effectively while mitigating overfitting. • Evaluation Metrics: • ROC-AUC: Measures overall classification performance (AUC = 50% indicates random guessing). • Hedge Portfolio Return: Constructs long–short portfolios based on predictions and computes size-adjusted annualized excess returns.

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TradingView provides financial estimates and actuals for Orion Engineered Carbons SA, allowing users to compare reported data across income statements, balance sheets, and valuation metrics.

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