September 14 news
In the lithium carbonate trading market, inventory data is the most relied-upon fundamental indicator for industry practitioners. Most people assume the simple logic that "prices rise when inventory falls and prices fall when inventory rises," but recent adjustments in the inventory reporting method, which led to a significant drop in lithium carbonate prices, have sparked discussions on whether inventory data can truly anchor the logic of price fluctuations. According to the commodity market analysis system, since the adjustment in institutional inventory reporting on September 3, the benchmark price of lithium carbonate has fallen from 150,000 CNY/ton to 135,000 CNY/ton, a decrease of over 10%. In fact, there are favorable factors in the fundamentals of lithium carbonate. Relying solely on lithium carbonate inventory data only reflects the surface of supply and demand, rather than the core of pricing.
The core flaw of inventory data is the instability of statistical standards. Currently, the mainstream inventory data in the market mostly comes from weekly statistics by third-party institutions, with the sample range and statistical targets often being adjusted. Recently, there was a case in the industry where the statistical standards were expanded. After adding the hidden inventory of new traders and less common lithium production sources, the inventory data surged by nearly 100,000 tons in the short term, directly triggering market panic and leading to a significant drop in lithium prices. However, this decline was not due to a real oversupply but rather a "false trend" caused by changes in data statistical rules. This frequent interference of data noise in the market has rendered single inventory data useless for trend analysis.
Second, the total inventory level does not reflect the inventory structure, and changes in the total volume can easily lead to misleading market assessments. Many industry participants focus solely on the overall increase or decrease in inventory, while overlooking the diverging patterns of inventory levels across upstream, midstream, and downstream segments. Lithium carbonate inventory is divided into upstream smelter inventories, midstream trader inventories, downstream battery manufacturer inventories, and futures warehouse receipts—each segment carries vastly different market implications. When upstream smelters accumulate inventory, it signals sluggish shipments and weak spot demand, constituting a genuine negative factor. Downstream replenishment of essential inventories and terminal consumption of existing stock represent healthy destocking processes with limited upward price support. Meanwhile, rising futures warehouse receipts are more often driven by hedging activities rather than a collapse in terminal demand. Currently, the market frequently exhibits a divergent pattern: industrial inventories remain low, while trade and circulation inventories tend to be relatively high. Although the total inventory volume appears stable, the structural imbalance makes it impossible for a single aggregate figure to accurately reflect the true supply-demand dynamics.
Lithium carbonate prices trade on expectations, not current inventory. Inventory data is a lagging historical result, reflecting the supply and demand situation over a past period, while lithium price trends are driven by forward supply and demand expectations. The current lithium battery industry is influenced by multiple factors such as new energy production schedules, energy storage policies, new capacity releases, and end-user consumption demands, leading to extremely rapid fluctuations in expectations. Even if current inventories continue to decline, if the market anticipates a concentrated release of new capacity and future oversupply, prices will still face downward pressure; conversely, if inventories accumulate slightly in the short term but peak season demand picks up and the supply gap widens, the market will still rebound. Recent changes in expectations, such as tighter approval for energy storage and fluctuations in downstream production schedules, have repeatedly reversed short-term market trends brought about by inventory data, fully confirming that expectations take precedence over the current inventory situation.
Of course, inventory data is not without value; it is an important auxiliary verification indicator rather than a single basis for judgment. When the market trend is clear, inventory changes can reinforce the logic of increases or decreases: a continuous reduction in inventory combined with a recovery in demand makes the upward trend more stable; continuous inventory accumulation combined with weak demand makes the downward trend more persistent.
Overall, lithium carbonate inventory data alone cannot define price increases or decreases. Simply applying the straightforward logic of inventory fluctuations can easily lead to misjudgment of market conditions. A truly comprehensive analysis requires eliminating statistical noise, breaking down inventory structures, and combining long-term supply and demand expectations. By integrating lagging inventory data with forward-looking industry trends, one can penetrate market volatility and grasp the true rhythm of lithium carbonate prices. At the same time, SpotLink can be used to detect the trend of spot price changes in advance. SpotLink relies on two mature methodologies—mean difference analysis and the five-level cycle price method—to output price trend predictions.
Average Difference Analysis Method: Automatically calculates the 5-day, 10-day, and 20-day average difference indicators. Based on the direction of change in these three indicators, it objectively generates clear signals such as "definite upward trend," "definite downward trend," "market oscillation," and "stagflation warning." When the directions of the three average differences are consistent, it indicates that a clear trend has been established. Conversely, when the indicator directions diverge, it signals risks of market oscillation or stagflation, transforming vague market perceptions into reproducible, objective signals.
Cycle Price Quintile Positioning Method: Automatically determines the current price's position within the historical cycle, categorizing it into one of five quintiles: low, lower-middle, middle, upper-middle, or high. This allows for a clear assessment of the potential for increases or decreases, and helps to identify the intervals of potential risk and opportunity.
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